{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solar home electricity data\n",
    "\n",
    "Source: http://www.ausgrid.com.au/Common/About-us/Corporate-information/Data-to-share/Solar-home-electricity-data.aspx\n",
    "\n",
    "PH April-May 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.20.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname = 'data/Solar home 2011-2012.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"2011-2012 Solar home electricity data - Before using this data, please read the attached pdf document \"\"Ausgrid solar home electricity data notes (Aug 2014).pdf\"\"\",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,\r",
      "\r\n",
      "Customer,Generator Capacity,Postcode,Consumption Category,date,0:30,1:00,1:30,2:00,2:30,3:00,3:30,4:00,4:30,5:00,5:30,6:00,6:30,7:00,7:30,8:00,8:30,9:00,9:30,10:00,10:30,11:00,11:30,12:00,12:30,13:00,13:30,14:00,14:30,15:00,15:30,16:00,16:30,17:00,17:30,18:00,18:30,19:00,19:30,20:00,20:30,21:00,21:30,22:00,22:30,23:00,23:30,0:00,Row Quality\r",
      "\r\n",
      "1,3.78,2076,CL,1/07/2011,1.25,1.25,1.25,0.625,0.219,1.013,0,0,0.125,0.894,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.063,\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 3 \"$fname\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse CSV\n",
    "\n",
    "(nb: parsing date is quite long):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "270304"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw = pd.read_csv(fname, skiprows=1,\n",
    "                    parse_dates=['date'], dayfirst=True,\n",
    "                    na_filter=False, dtype={'Row Quality': str})\n",
    "len(d_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview\n",
    "\n",
    "rows seems ordered by: Customer / Consumption Category / date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Generator Capacity</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Consumption Category</th>\n",
       "      <th>date</th>\n",
       "      <th>0:30</th>\n",
       "      <th>1:00</th>\n",
       "      <th>1:30</th>\n",
       "      <th>2:00</th>\n",
       "      <th>2:30</th>\n",
       "      <th>...</th>\n",
       "      <th>20:00</th>\n",
       "      <th>20:30</th>\n",
       "      <th>21:00</th>\n",
       "      <th>21:30</th>\n",
       "      <th>22:00</th>\n",
       "      <th>22:30</th>\n",
       "      <th>23:00</th>\n",
       "      <th>23:30</th>\n",
       "      <th>0:00</th>\n",
       "      <th>Row Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2076</td>\n",
       "      <td>CL</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.063</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2076</td>\n",
       "      <td>GC</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>1.077</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.162</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2076</td>\n",
       "      <td>GG</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2076</td>\n",
       "      <td>CL</td>\n",
       "      <td>2011-07-02</td>\n",
       "      <td>1.238</td>\n",
       "      <td>1.244</td>\n",
       "      <td>1.194</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.888</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.075</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2076</td>\n",
       "      <td>GC</td>\n",
       "      <td>2011-07-02</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.084</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer  Generator Capacity  Postcode Consumption Category       date  \\\n",
       "0         1                3.78      2076                   CL 2011-07-01   \n",
       "1         1                3.78      2076                   GC 2011-07-01   \n",
       "2         1                3.78      2076                   GG 2011-07-01   \n",
       "3         1                3.78      2076                   CL 2011-07-02   \n",
       "4         1                3.78      2076                   GC 2011-07-02   \n",
       "\n",
       "    0:30   1:00   1:30   2:00   2:30     ...       20:00  20:30  21:00  21:30  \\\n",
       "0  1.250  1.250  1.250  0.625  0.219     ...       0.000  0.000  0.000  0.000   \n",
       "1  1.077  0.669  0.674  0.615  0.321     ...       0.276  0.241  0.168  0.234   \n",
       "2  0.000  0.000  0.000  0.000  0.000     ...       0.000  0.000  0.000  0.000   \n",
       "3  1.238  1.244  1.194  0.000  0.888     ...       0.000  0.000  0.000  0.000   \n",
       "4  0.476  0.130  0.044  0.094  0.032     ...       0.234  0.306  0.183  0.240   \n",
       "\n",
       "   22:00  22:30  23:00  23:30   0:00  Row Quality  \n",
       "0  0.000  0.000  0.000  0.000  1.063               \n",
       "1  0.164  0.639  0.118  0.219  0.162               \n",
       "2  0.000  0.000  0.000  0.000  0.000               \n",
       "3  0.000  0.000  0.000  0.000  1.075               \n",
       "4  0.170  0.506  0.224  0.088  0.084               \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Generator Capacity</th>\n",
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       "      <th>Consumption Category</th>\n",
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       "      <th>270299</th>\n",
       "      <td>300</td>\n",
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       "      <td>0.082</td>\n",
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       "      <td>0.084</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.408</td>\n",
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       "      <td>0.517</td>\n",
       "      <td>0.393</td>\n",
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       "    <tr>\n",
       "      <th>270300</th>\n",
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       "      <td>3.36</td>\n",
       "      <td>2086</td>\n",
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       "      <td>2012-06-29</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>300</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2086</td>\n",
       "      <td>CL</td>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>2.110</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.621</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>270302</th>\n",
       "      <td>300</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2086</td>\n",
       "      <td>GC</td>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.096</td>\n",
       "      <td>...</td>\n",
       "      <td>1.526</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.235</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270303</th>\n",
       "      <td>300</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2086</td>\n",
       "      <td>GG</td>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td></td>\n",
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       "<p>5 rows × 54 columns</p>\n",
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      ],
      "text/plain": [
       "        Customer  Generator Capacity  Postcode Consumption Category  \\\n",
       "270299       300                3.36      2086                   GC   \n",
       "270300       300                3.36      2086                   GG   \n",
       "270301       300                3.36      2086                   CL   \n",
       "270302       300                3.36      2086                   GC   \n",
       "270303       300                3.36      2086                   GG   \n",
       "\n",
       "             date   0:30   1:00   1:30   2:00   2:30     ...       20:00  \\\n",
       "270299 2012-06-29  0.082  0.129  0.084  0.123  0.091     ...       0.496   \n",
       "270300 2012-06-29  0.000  0.000  0.000  0.000  0.000     ...       0.000   \n",
       "270301 2012-06-30  2.110  0.000  0.000  0.000  0.000     ...       0.000   \n",
       "270302 2012-06-30  0.247  0.193  0.109  0.115  0.096     ...       1.526   \n",
       "270303 2012-06-30  0.000  0.000  0.000  0.000  0.000     ...       0.000   \n",
       "\n",
       "        20:30  21:00  21:30  22:00  22:30  23:00  23:30   0:00  Row Quality  \n",
       "270299  0.387  0.357  0.387  0.408  0.527  0.517  0.393  0.226               \n",
       "270300  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000               \n",
       "270301  0.000  0.000  0.000  0.000  0.000  0.000  0.000  2.621               \n",
       "270302  0.799  0.822  0.244  0.200  0.204  0.131  0.358  0.235               \n",
       "270303  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000               \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Details on major columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Post codes: \n",
    "\n",
    "* 100 different codes\n",
    "* between 2008 and 2330, which corresponds to New South Wales (cf. [Postcodes_in_Australia](https://en.wikipedia.org/wiki/Postcodes_in_Australia)), as said by Ausgrid\n",
    "* Postcode geocoding: [Postcodes location.ipynb](Postcodes location.ipynb)\n",
    "* Geocoding output: [postcodes.csv](postcodes/postcodes.csv)\n",
    "\n",
    "![New South Wales](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/New_South_Wales_in_Australia.svg/267px-New_South_Wales_in_Australia.svg.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d_raw.Postcode.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2008, 2010, 2018, 2021, 2025, 2026, 2029, 2031, 2034, 2037, 2039, 2041, 2044, 2046, 2047, 2048, 2066, 2074, 2076, 2077, 2079, 2081, 2082, 2084, 2085, 2086, 2087, 2088, 2092, 2093, 2096, 2099, 2100, 2111, 2119, 2120, 2126, 2134, 2135, 2137, 2154, 2159, 2162, 2190, 2196, 2198, 2200, 2203, 2204, 2205, 2207, 2208, 2209, 2211, 2216, 2218, 2219, 2220, 2224, 2227, 2228, 2229, 2230, 2231, 2233, 2250, 2251, 2259, 2260, 2261, 2262, 2263, 2264, 2265, 2278, 2280, 2281, 2282, 2283, 2284, 2285, 2286, 2289, 2290, 2291, 2292, 2293, 2294, 2295, 2296, 2300, 2303, 2305, 2315, 2320, 2321, 2324, 2325, 2326, 2330]\n"
     ]
    }
   ],
   "source": [
    "print(sorted(d_raw.Postcode.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dates\n",
    "\n",
    "* `parse_date` option is working (although quite slow)\n",
    "* `dayfirst=True` is needed to avoid date inversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2011-07-01\n",
       "1   2011-07-01\n",
       "2   2011-07-01\n",
       "3   2011-07-02\n",
       "4   2011-07-02\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw.date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 880 ms, sys: 0 ns, total: 880 ms\n",
      "Wall time: 877 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-07-01 00:00:00')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time min(d_raw.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n",
      "Wall time: 2.46 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-07-01 00:00:00')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time d_raw.date.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2011-07-01 00:00:00'), Timestamp('2012-06-30 00:00:00'))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d0, d1 = d_raw.date.min(), d_raw.date.max()\n",
    "d0, d1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Row Quality:\n",
    "\n",
    "Ausgrid description:\n",
    "> (Blank) = every half hour value in the row is the actual electricity\n",
    "recorded by the meter in the half hour\n",
    ">\n",
    "> NA = Non-Actual where some or all of the half hour values in the\n",
    "row are estimates or substitutes of the electricity consumed or\n",
    "generated\n",
    "\n",
    "Specifying the `dtype={'Row Quality': str}` is needed to avoid a columns of dtype float, filled with NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', 'NA'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw['Row Quality'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      270204\n",
       "NA       100\n",
       "Name: Row Quality, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw['Row Quality'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Customers\n",
    "\n",
    "300 unique values, from 1 to 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d_raw['Customer'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
       "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
       "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
       "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
       "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
       "        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
       "       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n",
       "       118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n",
       "       131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n",
       "       144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n",
       "       157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n",
       "       170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n",
       "       183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n",
       "       209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n",
       "       222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n",
       "       235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n",
       "       248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n",
       "       261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n",
       "       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n",
       "       287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw['Customer'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Consumption Category (aka measurement channel)\n",
    "\n",
    "From Ausgrid description:\n",
    "\n",
    "> **GC** = General Consumption for electricity supplied all the time\n",
    "(primary tariff, either inclining block or time of use rates),\n",
    "excluding solar generation and controlled load supply\n",
    "\n",
    "> **CL** = Controlled Load Consumption (Off peak 1 or 2 tariffs)\n",
    "\n",
    "> **GG** = Gross Generation for electricity generated by the solar\n",
    "system with a gross metering configuration, measured separately\n",
    "to household loads\n",
    "\n",
    "Observation: only half as much CL as GG and GC.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CL', 'GC', 'GG'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw['Consumption Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GG    109800\n",
       "GC    109800\n",
       "CL     50704\n",
       "Name: Consumption Category, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw['Consumption Category'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Completeness of the record\n",
    "\n",
    "there are some missing records in the dataframe:\n",
    "\n",
    "* GG and GC is complete\n",
    "* Some 54% of CL is missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.461785063752277"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d_raw)/366/300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GG    1.000000\n",
       "GC    1.000000\n",
       "CL    0.461785\n",
       "Name: Consumption Category, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_raw['Consumption Category'].value_counts()/300/366"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Reshaping the data set.\n",
    "\n",
    "Objective:\n",
    "\n",
    "* Stack the time, to get proper timestamp combining day and hour\n",
    "* pivot the customer and consumption category as columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create datetime index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-06-30 00:00:00')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-07-01 00:00:00')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 + Day(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the index, with **\"left\" convention** (start of the 30min interval), to make slicing for a given day easier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-07-01 00:00:00', '2011-07-01 00:30:00',\n",
       "               '2011-07-01 01:00:00', '2011-07-01 01:30:00',\n",
       "               '2011-07-01 02:00:00', '2011-07-01 02:30:00',\n",
       "               '2011-07-01 03:00:00', '2011-07-01 03:30:00',\n",
       "               '2011-07-01 04:00:00', '2011-07-01 04:30:00',\n",
       "               ...\n",
       "               '2012-06-30 19:00:00', '2012-06-30 19:30:00',\n",
       "               '2012-06-30 20:00:00', '2012-06-30 20:30:00',\n",
       "               '2012-06-30 21:00:00', '2012-06-30 21:30:00',\n",
       "               '2012-06-30 22:00:00', '2012-06-30 22:30:00',\n",
       "               '2012-06-30 23:00:00', '2012-06-30 23:30:00'],\n",
       "              dtype='datetime64[ns]', length=17568, freq='30T')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.date_range(d0, d1 + Day(1), freq='30T', closed='left')\n",
    "index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colums: Customer/Channel\n",
    "\n",
    "using a MultiIndex, cf. [MultiIndex / Advanced Indexing](http://pandas.pydata.org/pandas-docs/stable/advanced.html) doc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300]\n"
     ]
    }
   ],
   "source": [
    "customers = sorted(d_raw.Customer.unique())\n",
    "print(customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CL', 'GC', 'GG'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channels = d_raw['Consumption Category'].unique()\n",
    "channels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customized channel order:\n",
    "\n",
    "* Consumption first (GC)\n",
    "* PV Generation second (GG)\n",
    "* CL third, because it is not always present (small trick to preserve the channel color in plots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "channels = ['GC', 'GG', 'CL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300], ['CL', 'GC', 'GG']],\n",
       "           labels=[[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 26, 27, 27, 27, 28, 28, 28, 29, 29, 29, 30, 30, 30, 31, 31, 31, 32, 32, 32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 36, 36, 36, 37, 37, 37, 38, 38, 38, 39, 39, 39, 40, 40, 40, 41, 41, 41, 42, 42, 42, 43, 43, 43, 44, 44, 44, 45, 45, 45, 46, 46, 46, 47, 47, 47, 48, 48, 48, 49, 49, 49, 50, 50, 50, 51, 51, 51, 52, 52, 52, 53, 53, 53, 54, 54, 54, 55, 55, 55, 56, 56, 56, 57, 57, 57, 58, 58, 58, 59, 59, 59, 60, 60, 60, 61, 61, 61, 62, 62, 62, 63, 63, 63, 64, 64, 64, 65, 65, 65, 66, 66, 66, 67, 67, 67, 68, 68, 68, 69, 69, 69, 70, 70, 70, 71, 71, 71, 72, 72, 72, 73, 73, 73, 74, 74, 74, 75, 75, 75, 76, 76, 76, 77, 77, 77, 78, 78, 78, 79, 79, 79, 80, 80, 80, 81, 81, 81, 82, 82, 82, 83, 83, 83, 84, 84, 84, 85, 85, 85, 86, 86, 86, 87, 87, 87, 88, 88, 88, 89, 89, 89, 90, 90, 90, 91, 91, 91, 92, 92, 92, 93, 93, 93, 94, 94, 94, 95, 95, 95, 96, 96, 96, 97, 97, 97, 98, 98, 98, 99, 99, 99, 100, 100, 100, 101, 101, 101, 102, 102, 102, 103, 103, 103, 104, 104, 104, 105, 105, 105, 106, 106, 106, 107, 107, 107, 108, 108, 108, 109, 109, 109, 110, 110, 110, 111, 111, 111, 112, 112, 112, 113, 113, 113, 114, 114, 114, 115, 115, 115, 116, 116, 116, 117, 117, 117, 118, 118, 118, 119, 119, 119, 120, 120, 120, 121, 121, 121, 122, 122, 122, 123, 123, 123, 124, 124, 124, 125, 125, 125, 126, 126, 126, 127, 127, 127, 128, 128, 128, 129, 129, 129, 130, 130, 130, 131, 131, 131, 132, 132, 132, 133, 133, 133, 134, 134, 134, 135, 135, 135, 136, 136, 136, 137, 137, 137, 138, 138, 138, 139, 139, 139, 140, 140, 140, 141, 141, 141, 142, 142, 142, 143, 143, 143, 144, 144, 144, 145, 145, 145, 146, 146, 146, 147, 147, 147, 148, 148, 148, 149, 149, 149, 150, 150, 150, 151, 151, 151, 152, 152, 152, 153, 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0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0]],\n",
       "           names=['Customer', 'Channel'])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = pd.MultiIndex.from_product(\n",
    "    (customers, channels), names=['Customer', 'Channel'])\n",
    "columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the new DataFrame\n",
    "\n",
    "starting from a blank DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "empty_cols = pd.MultiIndex(\n",
    "    levels=[customers, channels],\n",
    "    labels=[[],[]],\n",
    "    names=['Customer', 'Channel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>2011-07-01 00:00:00</th>\n",
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       "      <th>2011-07-01 00:30:00</th>\n",
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       "      <th>2011-07-01 02:00:00</th>\n",
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       "      <th>2011-07-01 02:30:00</th>\n",
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       "      <th>2011-07-01 03:00:00</th>\n",
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       "      <th>2011-07-01 03:30:00</th>\n",
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       "      <th>2011-07-01 04:00:00</th>\n",
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       "      <th>2011-07-01 05:00:00</th>\n",
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       "      <th>2011-07-01 05:30:00</th>\n",
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       "      <th>2011-07-01 06:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2011-07-01 06:30:00</th>\n",
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       "      <th>2011-07-01 07:00:00</th>\n",
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       "      <th>2011-07-01 07:30:00</th>\n",
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       "      <th>2011-07-01 08:30:00</th>\n",
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       "      <th>2011-07-01 09:00:00</th>\n",
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       "      <th>2011-07-01 09:30:00</th>\n",
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       "      <th>2011-07-01 10:00:00</th>\n",
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       "      <th>2011-07-01 10:30:00</th>\n",
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       "      <th>2011-07-01 11:00:00</th>\n",
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       "      <th>2011-07-01 11:30:00</th>\n",
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       "      <th>2011-07-01 14:30:00</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2012-06-30 09:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 09:30:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 10:00:00</th>\n",
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       "      <th>2012-06-30 10:30:00</th>\n",
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       "      <th>2012-06-30 11:30:00</th>\n",
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       "      <th>2012-06-30 12:00:00</th>\n",
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       "      <th>2012-06-30 12:30:00</th>\n",
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       "      <th>2012-06-30 14:30:00</th>\n",
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       "      <th>2012-06-30 15:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2012-06-30 15:30:00</th>\n",
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       "    <tr>\n",
       "      <th>2012-06-30 16:00:00</th>\n",
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       "      <th>2012-06-30 16:30:00</th>\n",
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       "      <th>2012-06-30 17:00:00</th>\n",
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       "      <th>2012-06-30 17:30:00</th>\n",
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       "      <th>2012-06-30 18:00:00</th>\n",
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       "      <th>2012-06-30 18:30:00</th>\n",
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       "      <th>2012-06-30 20:30:00</th>\n",
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       "      <th>2012-06-30 21:30:00</th>\n",
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       "    <tr>\n",
       "      <th>2012-06-30 22:00:00</th>\n",
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       "      <th>2012-06-30 22:30:00</th>\n",
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       "      <th>2012-06-30 23:00:00</th>\n",
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       "      <th>2012-06-30 23:30:00</th>\n",
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       "<p>17568 rows × 0 columns</p>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [2011-07-01 00:00:00, 2011-07-01 00:30:00, 2011-07-01 01:00:00, 2011-07-01 01:30:00, 2011-07-01 02:00:00, 2011-07-01 02:30:00, 2011-07-01 03:00:00, 2011-07-01 03:30:00, 2011-07-01 04:00:00, 2011-07-01 04:30:00, 2011-07-01 05:00:00, 2011-07-01 05:30:00, 2011-07-01 06:00:00, 2011-07-01 06:30:00, 2011-07-01 07:00:00, 2011-07-01 07:30:00, 2011-07-01 08:00:00, 2011-07-01 08:30:00, 2011-07-01 09:00:00, 2011-07-01 09:30:00, 2011-07-01 10:00:00, 2011-07-01 10:30:00, 2011-07-01 11:00:00, 2011-07-01 11:30:00, 2011-07-01 12:00:00, 2011-07-01 12:30:00, 2011-07-01 13:00:00, 2011-07-01 13:30:00, 2011-07-01 14:00:00, 2011-07-01 14:30:00, 2011-07-01 15:00:00, 2011-07-01 15:30:00, 2011-07-01 16:00:00, 2011-07-01 16:30:00, 2011-07-01 17:00:00, 2011-07-01 17:30:00, 2011-07-01 18:00:00, 2011-07-01 18:30:00, 2011-07-01 19:00:00, 2011-07-01 19:30:00, 2011-07-01 20:00:00, 2011-07-01 20:30:00, 2011-07-01 21:00:00, 2011-07-01 21:30:00, 2011-07-01 22:00:00, 2011-07-01 22:30:00, 2011-07-01 23:00:00, 2011-07-01 23:30:00, 2011-07-02 00:00:00, 2011-07-02 00:30:00, 2011-07-02 01:00:00, 2011-07-02 01:30:00, 2011-07-02 02:00:00, 2011-07-02 02:30:00, 2011-07-02 03:00:00, 2011-07-02 03:30:00, 2011-07-02 04:00:00, 2011-07-02 04:30:00, 2011-07-02 05:00:00, 2011-07-02 05:30:00, 2011-07-02 06:00:00, 2011-07-02 06:30:00, 2011-07-02 07:00:00, 2011-07-02 07:30:00, 2011-07-02 08:00:00, 2011-07-02 08:30:00, 2011-07-02 09:00:00, 2011-07-02 09:30:00, 2011-07-02 10:00:00, 2011-07-02 10:30:00, 2011-07-02 11:00:00, 2011-07-02 11:30:00, 2011-07-02 12:00:00, 2011-07-02 12:30:00, 2011-07-02 13:00:00, 2011-07-02 13:30:00, 2011-07-02 14:00:00, 2011-07-02 14:30:00, 2011-07-02 15:00:00, 2011-07-02 15:30:00, 2011-07-02 16:00:00, 2011-07-02 16:30:00, 2011-07-02 17:00:00, 2011-07-02 17:30:00, 2011-07-02 18:00:00, 2011-07-02 18:30:00, 2011-07-02 19:00:00, 2011-07-02 19:30:00, 2011-07-02 20:00:00, 2011-07-02 20:30:00, 2011-07-02 21:00:00, 2011-07-02 21:30:00, 2011-07-02 22:00:00, 2011-07-02 22:30:00, 2011-07-02 23:00:00, 2011-07-02 23:30:00, 2011-07-03 00:00:00, 2011-07-03 00:30:00, 2011-07-03 01:00:00, 2011-07-03 01:30:00, ...]\n",
       "\n",
       "[17568 rows x 0 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(index=index, columns=empty_cols)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill the columns:\n",
    "\n",
    "notice: \n",
    "\n",
    "* it is way slower when the 3x300 columns are pre-assigned in the DataFrame creation\n",
    "* but the MultiIndex is lost when pre-assignment is not done (empty columns)\n",
    "* → solution which seems to work: using an empty MultiIndex (customers/channel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, "
     ]
    }
   ],
   "source": [
    "missing_records = []\n",
    "\n",
    "for c in customers:\n",
    "    d_c = d_raw[d_raw.Customer == c]\n",
    "    \n",
    "    # TODO: save the row quality\n",
    "    print(c, end=', ')\n",
    "    \n",
    "    for ch in channels:\n",
    "        d_c_ch = d_c[d_c['Consumption Category'] == ch]\n",
    "        ts = d_c_ch.iloc[:,5:-1].values.ravel()\n",
    "        if len(ts) != len(index):\n",
    "            missing_records.append((c,ch, len(ts)))\n",
    "        else:\n",
    "            df[c, ch] = ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis of missing/imcomplete records\n",
    "\n",
    "Observation: out of 162 missing records (over a potential of 900):\n",
    "\n",
    "* 161 are the complete absence of the CL channel\n",
    "* 1 is an incomplete CL channe (from 2011-07-01 to 2012-01-12): maybe a change of tariff ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "900"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missing_records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "161"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_empty_CL = 0\n",
    "missing_records_others = []\n",
    "for (c, ch, len_ts) in missing_records:\n",
    "    if ch=='CL' and len_ts==0:\n",
    "        n_empty_CL += 1\n",
    "    else:\n",
    "        missing_records_others.append((c, ch, len_ts))\n",
    "n_empty_CL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(27, 'CL', 9408)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_records_others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24522   2011-07-01\n",
       "25107   2012-01-12\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_27_CL = d_raw[(d_raw.Customer == 27) & (d_raw['Consumption Category'] == 'CL')]\n",
    "d_27_CL['date'].iloc[[0,-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "900"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns) + len(missing_records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th colspan=\"3\" halign=\"left\">2</th>\n",
       "      <th colspan=\"3\" halign=\"left\">3</th>\n",
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       "      <th colspan=\"2\" halign=\"left\">299</th>\n",
       "      <th colspan=\"3\" halign=\"left\">300</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel</th>\n",
       "      <th>GC</th>\n",
       "      <th>GG</th>\n",
       "      <th>CL</th>\n",
       "      <th>GC</th>\n",
       "      <th>GG</th>\n",
       "      <th>CL</th>\n",
       "      <th>GC</th>\n",
       "      <th>GG</th>\n",
       "      <th>CL</th>\n",
       "      <th>GC</th>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2011-07-01 00:00:00</th>\n",
       "      <td>1.077</td>\n",
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       "      <td>2.008</td>\n",
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       "      <td>0.140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.533</td>\n",
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       "    <tr>\n",
       "      <th>2011-07-01 00:30:00</th>\n",
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       "      <td>0.218</td>\n",
       "      <td>0.107</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.265</td>\n",
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       "    <tr>\n",
       "      <th>2011-07-01 01:00:00</th>\n",
       "      <td>0.674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 01:30:00</th>\n",
       "      <td>0.615</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.311</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>2011-07-01 02:00:00</th>\n",
       "      <td>0.321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219</td>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 738 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Customer               1                  2                3                \\\n",
       "Channel                 GC   GG     CL     GC   GG   CL     GC   GG     CL   \n",
       "2011-07-01 00:00:00  1.077  0.0  1.250  0.276  0.0  0.0  0.060  0.0  2.008   \n",
       "2011-07-01 00:30:00  0.669  0.0  1.250  0.134  0.0  0.0  0.062  0.0  0.218   \n",
       "2011-07-01 01:00:00  0.674  0.0  1.250  0.169  0.0  0.0  0.060  0.0  0.000   \n",
       "2011-07-01 01:30:00  0.615  0.0  0.625  0.185  0.0  0.0  0.059  0.0  0.000   \n",
       "2011-07-01 02:00:00  0.321  0.0  0.219  0.141  0.0  0.0  0.060  0.0  0.000   \n",
       "\n",
       "Customer               4    ...      297                298         299       \\\n",
       "Channel                 GC  ...       GC   GG     CL     GC   GG     GC   GG   \n",
       "2011-07-01 00:00:00  0.104  ...    0.268  0.0  0.000  0.100  0.0  0.140  0.0   \n",
       "2011-07-01 00:30:00  0.107  ...    0.329  0.0  0.000  0.068  0.0  0.160  0.0   \n",
       "2011-07-01 01:00:00  0.104  ...    0.306  0.0  0.000  0.068  0.0  0.116  0.0   \n",
       "2011-07-01 01:30:00  0.108  ...    0.233  0.0  1.311  0.068  0.0  0.162  0.0   \n",
       "2011-07-01 02:00:00  0.110  ...    0.265  0.0  0.000  0.069  0.0  0.138  0.0   \n",
       "\n",
       "Customer               300              \n",
       "Channel                 GC   GG     CL  \n",
       "2011-07-01 00:00:00  1.034  0.0  2.533  \n",
       "2011-07-01 00:30:00  0.631  0.0  0.265  \n",
       "2011-07-01 01:00:00  0.119  0.0  0.000  \n",
       "2011-07-01 01:30:00  0.098  0.0  0.000  \n",
       "2011-07-01 02:00:00  0.109  0.0  0.000  \n",
       "\n",
       "[5 rows x 738 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>Channel</th>\n",
       "      <th>GC</th>\n",
       "      <th>GG</th>\n",
       "      <th>CL</th>\n",
       "      <th>GC</th>\n",
       "      <th>GG</th>\n",
       "      <th>CL</th>\n",
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       "      <th>2012-06-30 21:30:00</th>\n",
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       "      <td>0.368</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.814</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>2012-06-30 22:30:00</th>\n",
       "      <td>0.716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.369</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.834</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>2012-06-30 23:00:00</th>\n",
       "      <td>0.999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.086</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.915</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 23:30:00</th>\n",
       "      <td>0.916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.063</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 738 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Customer               1                  2                3                \\\n",
       "Channel                 GC   GG     CL     GC   GG   CL     GC     GG   CL   \n",
       "2012-06-30 21:30:00  0.622  0.0  0.000  0.368  0.0  0.0  0.066  0.000  0.0   \n",
       "2012-06-30 22:00:00  0.509  0.0  0.000  0.305  0.0  0.0  0.768  0.000  0.0   \n",
       "2012-06-30 22:30:00  0.716  0.0  0.000  0.285  0.0  0.0  0.966  0.000  0.0   \n",
       "2012-06-30 23:00:00  0.999  0.0  0.000  0.286  0.0  0.0  0.957  0.006  0.0   \n",
       "2012-06-30 23:30:00  0.916  0.0  1.063  0.188  0.0  0.0  0.902  0.000  0.0   \n",
       "\n",
       "Customer               4    ...      297                298         299       \\\n",
       "Channel                 GC  ...       GC   GG     CL     GC   GG     GC   GG   \n",
       "2012-06-30 21:30:00  0.159  ...    0.366  0.0  0.000  0.173  0.0  1.095  0.0   \n",
       "2012-06-30 22:00:00  0.134  ...    0.458  0.0  0.814  0.154  0.0  0.946  0.0   \n",
       "2012-06-30 22:30:00  0.101  ...    0.305  0.0  2.369  0.150  0.0  1.834  0.0   \n",
       "2012-06-30 23:00:00  0.086  ...    0.165  0.0  1.915  0.142  0.0  1.719  0.0   \n",
       "2012-06-30 23:30:00  0.082  ...    0.206  0.0  0.000  0.145  0.0  1.412  0.0   \n",
       "\n",
       "Customer               300              \n",
       "Channel                 GC   GG     CL  \n",
       "2012-06-30 21:30:00  0.200  0.0  0.000  \n",
       "2012-06-30 22:00:00  0.204  0.0  0.000  \n",
       "2012-06-30 22:30:00  0.131  0.0  0.000  \n",
       "2012-06-30 23:00:00  0.358  0.0  0.000  \n",
       "2012-06-30 23:30:00  0.235  0.0  2.621  \n",
       "\n",
       "[5 rows x 738 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unit conversion: kWh → kW\n",
    "\n",
    "Energy records in the original CSV file is in kWh on the 30 min interval. Convert to kW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel\n",
       "GC    0.292210\n",
       "GG    0.269105\n",
       "CL    0.146694\n",
       "dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df *= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel\n",
       "GC    0.584421\n",
       "GG    0.538209\n",
       "CL    0.293388\n",
       "dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Howto work with the reshaped DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3a) select *one* customer, with all its 3 channels\n",
    "\n",
    "this easy because `customer` is the first level of the MultiIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Channel</th>\n",
       "      <th>GC</th>\n",
       "      <th>GG</th>\n",
       "      <th>CL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-07-01 00:00:00</th>\n",
       "      <td>2.154</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 00:30:00</th>\n",
       "      <td>1.338</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 01:00:00</th>\n",
       "      <td>1.348</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 01:30:00</th>\n",
       "      <td>1.230</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 02:00:00</th>\n",
       "      <td>0.642</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 02:30:00</th>\n",
       "      <td>0.194</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 03:00:00</th>\n",
       "      <td>1.054</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 03:30:00</th>\n",
       "      <td>0.140</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 04:00:00</th>\n",
       "      <td>0.098</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 04:30:00</th>\n",
       "      <td>0.170</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 05:00:00</th>\n",
       "      <td>0.960</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 05:30:00</th>\n",
       "      <td>0.168</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 06:00:00</th>\n",
       "      <td>0.068</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 06:30:00</th>\n",
       "      <td>0.202</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 07:00:00</th>\n",
       "      <td>0.112</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 07:30:00</th>\n",
       "      <td>0.242</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 08:00:00</th>\n",
       "      <td>0.300</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 08:30:00</th>\n",
       "      <td>0.390</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 09:00:00</th>\n",
       "      <td>0.246</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 09:30:00</th>\n",
       "      <td>0.116</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 10:00:00</th>\n",
       "      <td>0.476</td>\n",
       "      <td>1.312</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 10:30:00</th>\n",
       "      <td>0.084</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 11:00:00</th>\n",
       "      <td>0.190</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 11:30:00</th>\n",
       "      <td>0.072</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 12:00:00</th>\n",
       "      <td>0.194</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 12:30:00</th>\n",
       "      <td>0.126</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 13:00:00</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 13:30:00</th>\n",
       "      <td>0.168</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 14:00:00</th>\n",
       "      <td>0.096</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 14:30:00</th>\n",
       "      <td>0.184</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 09:00:00</th>\n",
       "      <td>0.206</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 09:30:00</th>\n",
       "      <td>0.198</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 10:00:00</th>\n",
       "      <td>0.262</td>\n",
       "      <td>1.312</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 10:30:00</th>\n",
       "      <td>0.666</td>\n",
       "      <td>1.526</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 11:00:00</th>\n",
       "      <td>0.200</td>\n",
       "      <td>1.650</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 11:30:00</th>\n",
       "      <td>0.132</td>\n",
       "      <td>1.800</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 12:00:00</th>\n",
       "      <td>0.194</td>\n",
       "      <td>1.726</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 12:30:00</th>\n",
       "      <td>0.116</td>\n",
       "      <td>1.212</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 13:00:00</th>\n",
       "      <td>0.680</td>\n",
       "      <td>2.088</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 13:30:00</th>\n",
       "      <td>0.106</td>\n",
       "      <td>1.962</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 14:00:00</th>\n",
       "      <td>0.218</td>\n",
       "      <td>1.312</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 14:30:00</th>\n",
       "      <td>0.092</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 15:00:00</th>\n",
       "      <td>0.226</td>\n",
       "      <td>1.038</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 15:30:00</th>\n",
       "      <td>0.088</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 16:00:00</th>\n",
       "      <td>0.802</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 16:30:00</th>\n",
       "      <td>0.318</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 17:00:00</th>\n",
       "      <td>0.960</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 17:30:00</th>\n",
       "      <td>0.290</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 18:00:00</th>\n",
       "      <td>0.634</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 18:30:00</th>\n",
       "      <td>0.484</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 19:00:00</th>\n",
       "      <td>0.646</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 19:30:00</th>\n",
       "      <td>0.868</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 20:00:00</th>\n",
       "      <td>0.558</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 20:30:00</th>\n",
       "      <td>0.394</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 21:00:00</th>\n",
       "      <td>0.566</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 21:30:00</th>\n",
       "      <td>1.244</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 22:00:00</th>\n",
       "      <td>1.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 22:30:00</th>\n",
       "      <td>1.432</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 23:00:00</th>\n",
       "      <td>1.998</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 23:30:00</th>\n",
       "      <td>1.832</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17568 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Channel                 GC     GG     CL\n",
       "2011-07-01 00:00:00  2.154  0.000  2.500\n",
       "2011-07-01 00:30:00  1.338  0.000  2.500\n",
       "2011-07-01 01:00:00  1.348  0.000  2.500\n",
       "2011-07-01 01:30:00  1.230  0.000  1.250\n",
       "2011-07-01 02:00:00  0.642  0.000  0.438\n",
       "2011-07-01 02:30:00  0.194  0.000  2.026\n",
       "2011-07-01 03:00:00  1.054  0.000  0.000\n",
       "2011-07-01 03:30:00  0.140  0.000  0.000\n",
       "2011-07-01 04:00:00  0.098  0.000  0.250\n",
       "2011-07-01 04:30:00  0.170  0.000  1.788\n",
       "2011-07-01 05:00:00  0.960  0.000  0.000\n",
       "2011-07-01 05:30:00  0.168  0.000  0.000\n",
       "2011-07-01 06:00:00  0.068  0.000  0.000\n",
       "2011-07-01 06:30:00  0.202  0.000  0.000\n",
       "2011-07-01 07:00:00  0.112  0.000  0.000\n",
       "2011-07-01 07:30:00  0.242  0.150  0.000\n",
       "2011-07-01 08:00:00  0.300  0.138  0.000\n",
       "2011-07-01 08:30:00  0.390  0.250  0.000\n",
       "2011-07-01 09:00:00  0.246  0.526  0.000\n",
       "2011-07-01 09:30:00  0.116  0.988  0.000\n",
       "2011-07-01 10:00:00  0.476  1.312  0.000\n",
       "2011-07-01 10:30:00  0.084  0.988  0.000\n",
       "2011-07-01 11:00:00  0.190  0.976  0.000\n",
       "2011-07-01 11:30:00  0.072  0.776  0.000\n",
       "2011-07-01 12:00:00  0.194  0.562  0.000\n",
       "2011-07-01 12:30:00  0.126  0.112  0.000\n",
       "2011-07-01 13:00:00  0.150  0.350  0.000\n",
       "2011-07-01 13:30:00  0.168  0.376  0.000\n",
       "2011-07-01 14:00:00  0.096  0.476  0.000\n",
       "2011-07-01 14:30:00  0.184  0.400  0.000\n",
       "...                    ...    ...    ...\n",
       "2012-06-30 09:00:00  0.206  0.500  0.000\n",
       "2012-06-30 09:30:00  0.198  0.950  0.000\n",
       "2012-06-30 10:00:00  0.262  1.312  0.000\n",
       "2012-06-30 10:30:00  0.666  1.526  0.000\n",
       "2012-06-30 11:00:00  0.200  1.650  0.000\n",
       "2012-06-30 11:30:00  0.132  1.800  0.000\n",
       "2012-06-30 12:00:00  0.194  1.726  0.000\n",
       "2012-06-30 12:30:00  0.116  1.212  0.000\n",
       "2012-06-30 13:00:00  0.680  2.088  0.000\n",
       "2012-06-30 13:30:00  0.106  1.962  0.000\n",
       "2012-06-30 14:00:00  0.218  1.312  0.000\n",
       "2012-06-30 14:30:00  0.092  0.888  0.000\n",
       "2012-06-30 15:00:00  0.226  1.038  0.000\n",
       "2012-06-30 15:30:00  0.088  0.512  0.000\n",
       "2012-06-30 16:00:00  0.802  0.088  0.000\n",
       "2012-06-30 16:30:00  0.318  0.012  0.000\n",
       "2012-06-30 17:00:00  0.960  0.000  0.000\n",
       "2012-06-30 17:30:00  0.290  0.000  0.000\n",
       "2012-06-30 18:00:00  0.634  0.000  0.000\n",
       "2012-06-30 18:30:00  0.484  0.000  0.000\n",
       "2012-06-30 19:00:00  0.646  0.000  0.000\n",
       "2012-06-30 19:30:00  0.868  0.000  0.000\n",
       "2012-06-30 20:00:00  0.558  0.000  0.000\n",
       "2012-06-30 20:30:00  0.394  0.000  0.000\n",
       "2012-06-30 21:00:00  0.566  0.000  0.000\n",
       "2012-06-30 21:30:00  1.244  0.000  0.000\n",
       "2012-06-30 22:00:00  1.018  0.000  0.000\n",
       "2012-06-30 22:30:00  1.432  0.000  0.000\n",
       "2012-06-30 23:00:00  1.998  0.000  0.000\n",
       "2012-06-30 23:30:00  1.832  0.000  2.126\n",
       "\n",
       "[17568 rows x 3 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel\n",
       "GC    0.584421\n",
       "GG    0.538209\n",
       "CL    0.293388\n",
       "dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2oyClfBBoENMV7COBD3ZyXEu0pylIKQ1T6BG0k7zWi9dJ5SmUybS2Dx25j6b2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\nNjY2NjYWtlKwsbGxsbH4/wEdbJLaDwAu/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfc75640b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[1]['2011-07-01':'2011-07-03'].plot()\n",
    "plt.title('Customer 1 in July')\n",
    "plt.ylabel('kW')\n",
    "plt.savefig('Customer 1 2011-07 01-03.png', dpi=150);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wJDJslo7ZBpl7A+Gk0650Yi2SR4mkM+Dhm/v9tHr8RCTDevilbPD7/CE80fdv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\n0IQaN06bJWMP/0S3j8b4UvamFZrBMJCOf8bUMQA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfc5ecd128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[100]['2011-07-01':'2011-07-03'].plot()\n",
    "plt.ylabel('kW')\n",
    "plt.title('Customer 100 in July (No CL)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3b) select *one* channel, across *all* customers\n",
    "\n",
    "This is less easy because `channel` is the 2nd level of the MultiIndex.\n",
    "It requires using [cross-section](http://pandas.pydata.org/pandas-docs/stable/advanced.html#cross-section) indexing with `xs`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Customer</th>\n",
       "      <th>1</th>\n",
       "      <th>4</th>\n",
       "      <th>100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-07-01 00:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 00:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 01:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 01:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 02:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 02:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 03:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 03:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 04:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 04:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 05:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 05:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 06:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 06:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 07:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 07:30:00</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 08:00:00</th>\n",
       "      <td>0.138</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 08:30:00</th>\n",
       "      <td>0.250</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 09:00:00</th>\n",
       "      <td>0.526</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 09:30:00</th>\n",
       "      <td>0.988</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 10:00:00</th>\n",
       "      <td>1.312</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 10:30:00</th>\n",
       "      <td>0.988</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 11:00:00</th>\n",
       "      <td>0.976</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 11:30:00</th>\n",
       "      <td>0.776</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 12:00:00</th>\n",
       "      <td>0.562</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 12:30:00</th>\n",
       "      <td>0.112</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 13:00:00</th>\n",
       "      <td>0.350</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 13:30:00</th>\n",
       "      <td>0.376</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 14:00:00</th>\n",
       "      <td>0.476</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 14:30:00</th>\n",
       "      <td>0.400</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 09:00:00</th>\n",
       "      <td>0.500</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 09:30:00</th>\n",
       "      <td>0.950</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 10:00:00</th>\n",
       "      <td>1.312</td>\n",
       "      <td>0.438</td>\n",
       "      <td>0.862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 10:30:00</th>\n",
       "      <td>1.526</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 11:00:00</th>\n",
       "      <td>1.650</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 11:30:00</th>\n",
       "      <td>1.800</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 12:00:00</th>\n",
       "      <td>1.726</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 12:30:00</th>\n",
       "      <td>1.212</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 13:00:00</th>\n",
       "      <td>2.088</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 13:30:00</th>\n",
       "      <td>1.962</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 14:00:00</th>\n",
       "      <td>1.312</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 14:30:00</th>\n",
       "      <td>0.888</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 15:00:00</th>\n",
       "      <td>1.038</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 15:30:00</th>\n",
       "      <td>0.512</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 16:00:00</th>\n",
       "      <td>0.088</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 16:30:00</th>\n",
       "      <td>0.012</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 17:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 17:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 18:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 18:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 19:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 19:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 20:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 20:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 21:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 21:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 22:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 22:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 23:00:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 23:30:00</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17568 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Customer               1      4      100\n",
       "2011-07-01 00:00:00  0.000  0.000  0.000\n",
       "2011-07-01 00:30:00  0.000  0.000  0.000\n",
       "2011-07-01 01:00:00  0.000  0.000  0.000\n",
       "2011-07-01 01:30:00  0.000  0.000  0.000\n",
       "2011-07-01 02:00:00  0.000  0.000  0.000\n",
       "2011-07-01 02:30:00  0.000  0.000  0.000\n",
       "2011-07-01 03:00:00  0.000  0.000  0.000\n",
       "2011-07-01 03:30:00  0.000  0.000  0.000\n",
       "2011-07-01 04:00:00  0.000  0.000  0.000\n",
       "2011-07-01 04:30:00  0.000  0.000  0.000\n",
       "2011-07-01 05:00:00  0.000  0.000  0.000\n",
       "2011-07-01 05:30:00  0.000  0.000  0.000\n",
       "2011-07-01 06:00:00  0.000  0.000  0.000\n",
       "2011-07-01 06:30:00  0.000  0.000  0.000\n",
       "2011-07-01 07:00:00  0.000  0.000  0.000\n",
       "2011-07-01 07:30:00  0.150  0.012  0.076\n",
       "2011-07-01 08:00:00  0.138  0.026  0.338\n",
       "2011-07-01 08:30:00  0.250  0.112  0.412\n",
       "2011-07-01 09:00:00  0.526  0.250  0.550\n",
       "2011-07-01 09:30:00  0.988  0.288  0.600\n",
       "2011-07-01 10:00:00  1.312  0.350  0.238\n",
       "2011-07-01 10:30:00  0.988  0.500  0.362\n",
       "2011-07-01 11:00:00  0.976  0.400  0.426\n",
       "2011-07-01 11:30:00  0.776  0.500  0.226\n",
       "2011-07-01 12:00:00  0.562  0.250  0.062\n",
       "2011-07-01 12:30:00  0.112  0.176  0.062\n",
       "2011-07-01 13:00:00  0.350  0.238  0.062\n",
       "2011-07-01 13:30:00  0.376  0.250  0.012\n",
       "2011-07-01 14:00:00  0.476  0.150  0.000\n",
       "2011-07-01 14:30:00  0.400  0.188  0.012\n",
       "...                    ...    ...    ...\n",
       "2012-06-30 09:00:00  0.500  0.288  0.650\n",
       "2012-06-30 09:30:00  0.950  0.376  0.762\n",
       "2012-06-30 10:00:00  1.312  0.438  0.862\n",
       "2012-06-30 10:30:00  1.526  0.500  0.912\n",
       "2012-06-30 11:00:00  1.650  0.538  0.976\n",
       "2012-06-30 11:30:00  1.800  0.562  0.988\n",
       "2012-06-30 12:00:00  1.726  0.576  0.926\n",
       "2012-06-30 12:30:00  1.212  0.588  0.588\n",
       "2012-06-30 13:00:00  2.088  0.526  0.138\n",
       "2012-06-30 13:30:00  1.962  0.138  0.076\n",
       "2012-06-30 14:00:00  1.312  0.150  0.126\n",
       "2012-06-30 14:30:00  0.888  0.200  0.162\n",
       "2012-06-30 15:00:00  1.038  0.200  0.126\n",
       "2012-06-30 15:30:00  0.512  0.076  0.076\n",
       "2012-06-30 16:00:00  0.088  0.038  0.012\n",
       "2012-06-30 16:30:00  0.012  0.000  0.000\n",
       "2012-06-30 17:00:00  0.000  0.000  0.000\n",
       "2012-06-30 17:30:00  0.000  0.000  0.000\n",
       "2012-06-30 18:00:00  0.000  0.000  0.000\n",
       "2012-06-30 18:30:00  0.000  0.000  0.000\n",
       "2012-06-30 19:00:00  0.000  0.000  0.000\n",
       "2012-06-30 19:30:00  0.000  0.000  0.012\n",
       "2012-06-30 20:00:00  0.000  0.000  0.000\n",
       "2012-06-30 20:30:00  0.000  0.000  0.000\n",
       "2012-06-30 21:00:00  0.000  0.000  0.000\n",
       "2012-06-30 21:30:00  0.000  0.000  0.000\n",
       "2012-06-30 22:00:00  0.000  0.000  0.000\n",
       "2012-06-30 22:30:00  0.000  0.000  0.000\n",
       "2012-06-30 23:00:00  0.000  0.000  0.000\n",
       "2012-06-30 23:30:00  0.000  0.000  0.000\n",
       "\n",
       "[17568 rows x 3 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfGG = df.xs('GG', level='Channel', axis=1)\n",
    "dfGG[[1, 4, 100]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FkVYXShTyA7c/xBst/XzygvnT7ms16lWgWaFQpI4/FMEdCCdmKcSJN8Ub9Ki4Qr7w0uFe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfc5f71b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfGG[[1, 4, 100]]['2011-07-01':'2011-07-03'].plot()\n",
    "plt.title('PV production')\n",
    "plt.ylabel('kW')\n",
    "plt.savefig('PV production 2011-07 01-03.png', dpi=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Some stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4a) PV generation capacity of each customer\n",
    "\n",
    "observations:\n",
    "\n",
    "* Generation capacity is constant over 2011-2012 (no generation upgrade)\n",
    "* Mean at 1.68, *in agreement with data note* from Ausgrid (§ Summary statistics, p2)\n",
    "* Most (75%) are between 1 and 2 kW.\n",
    "* Some (2%) are around 6, 8 or 10 kW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Generator Capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>2</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270303</th>\n",
       "      <td>300</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Customer  Generator Capacity\n",
       "0              1                3.78\n",
       "1              1                3.78\n",
       "2000           2                1.62\n",
       "270303       300                3.36"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_cust_cap = d_raw[['Customer', 'Generator Capacity']]\n",
    "d_cust_cap.iloc[[0,1,2000, -1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if the generator capacity is constant for each customer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    300\n",
       "Name: Generator Capacity, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_cust_cap.groupby('Customer')['Generator Capacity'].nunique().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    300.000000\n",
       "mean       1.683300\n",
       "std        1.036846\n",
       "min        1.000000\n",
       "50%        1.500000\n",
       "80%        2.040000\n",
       "90%        2.728000\n",
       "98%        4.571200\n",
       "max        9.990000\n",
       "Name: Generator Capacity, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_cap = d_cust_cap.groupby('Customer')['Generator Capacity'].mean()\n",
    "gen_cap.describe(percentiles=[0.5, 0.8, 0.9, 0.980])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x7fbfc78b5080>]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kaelwCgRJapxB31OSFyX5XJI7ktye5JxJ1zRpSQ5IcnOST026lklLcliSK5N8LcmdSV47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\noOIA8kTeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfc78fa0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = gen_cap.hist(bins=20)\n",
    "ax.set(xlabel='PV gen capacity (kW)')\n",
    "#ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4b) Yield of PV generation \n",
    "\n",
    "* annual generation (kWh)\n",
    "* sun hours/year (kWh/kWp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check of one customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.7800000000000842"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_cap[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zcrKfdqbeRHhCH+YsqF2Nna3vSZTCHsa21i7c+s+lkamMUfjbvA0AgL5+NHPJ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\nY+xe8AXmN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfc527cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfGG[1].resample('1D').max().plot(label='daily max')\n",
    "plt.title('PV production (daily max) of customer 1 ')\n",
    "plt.ylabel('kW')\n",
    "plt.hlines(gen_cap[1], df.index[0], df.index[-1], linestyles='dashed', color='tab:red', label='PV capacity')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Global statistics of PV generation\n",
    "\n",
    "* Production mean and median *in agreement with data note* from Ausgrid (§ Summary statistics, p2)\n",
    "\n",
    "* Normalized by capacity: **1231 hours/y** averaged across all customers\n",
    "* All are above 800 hours/y, except 4 customers\n",
    "\n",
    "NB: the coast around Sidney's has a GHI between 4-5 kWh/m²/day, that is **1500-1800 kWh/m²/y**\n",
    "\n",
    "<img src='external/SolarGIS Australia GHI map south.png' alt='Australia GHI map' style='width:50em'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2083.180033333341, 1708.0875000000215)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = 0.5 # hours\n",
    "GG_year = dfGG.sum()*dt\n",
    "GG_year.mean(), GG_year.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1230.5549200014514"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_perf = GG_year/gen_cap\n",
    "pv_perf.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EPF3CeszMrETeRWNmlqlOC3wA35S0TtKSMgKZmVk5FBHtLyzNi4jNkg4DbgUujojbJsyz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\n/w9ttE/ic3S7nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfb4390ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pv_perf.hist(bins=30)\n",
    "plt.xlabel('yearly PV production kWh/kWp');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PV installations with bad performance:\n",
    "\n",
    "observation: bad performance relates to periods of blank production (maintenance issue or record issue ?)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer\n",
       "92     119.0\n",
       "220    461.0\n",
       "234    533.0\n",
       "239    527.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_lowpv = pv_perf.index[pv_perf<700]\n",
    "pv_perf[cust_lowpv].round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb4be3a58>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oqKxk5t134XjzTQZ++tOcWCPYVq2keE0oJTDZIu/Ytp3hZ54JbZDlvK4PqANI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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfb4bec9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfGG[cust_lowpv].resample('1W').mean().plot(style='-D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4c) Consumption average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice: without caring for NaN values, the average consumption **doesn't match** the data note from Ausgrid (§ Summary statistics, p2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfGC = df.xs('GC', level='Channel', axis=1)\n",
    "dfCL = df.xs('CL', level='Channel', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5738.998883333322, 1863.789572463766, 7602.788455797088, 7043.790789855059)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = 0.5 # hours\n",
    "GC_year = dfGC.sum()*dt\n",
    "CL_year = dfCL.sum()*dt\n",
    "\n",
    "GC_year.mean(), CL_year.mean(), GC_year.mean()+  CL_year.mean(), (GC_year +  CL_year).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(138, 300)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(CL_year), len(GC_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer\n",
       "1       7710.675\n",
       "2       8493.488\n",
       "3       3464.013\n",
       "4       5350.537\n",
       "5       4154.427\n",
       "6      11049.959\n",
       "7       6297.717\n",
       "8      12445.192\n",
       "9       5042.297\n",
       "10      6654.568\n",
       "11           NaN\n",
       "12           NaN\n",
       "13           NaN\n",
       "14      3636.917\n",
       "15      3283.148\n",
       "16           NaN\n",
       "17           NaN\n",
       "18      8150.175\n",
       "19      2617.498\n",
       "20           NaN\n",
       "21           NaN\n",
       "22           NaN\n",
       "23      6139.032\n",
       "24           NaN\n",
       "25           NaN\n",
       "26           NaN\n",
       "27           NaN\n",
       "28           NaN\n",
       "29      5505.936\n",
       "30      3781.780\n",
       "         ...    \n",
       "271    11093.339\n",
       "272     4133.785\n",
       "273          NaN\n",
       "274     9694.788\n",
       "275          NaN\n",
       "276          NaN\n",
       "277     4711.474\n",
       "278          NaN\n",
       "279    11464.558\n",
       "280          NaN\n",
       "281     5923.665\n",
       "282          NaN\n",
       "283     3438.182\n",
       "284     4461.087\n",
       "285     4956.869\n",
       "286    10382.334\n",
       "287    12452.352\n",
       "288          NaN\n",
       "289    12316.420\n",
       "290     7815.447\n",
       "291          NaN\n",
       "292          NaN\n",
       "293    12764.541\n",
       "294     8265.949\n",
       "295          NaN\n",
       "296          NaN\n",
       "297     6890.217\n",
       "298          NaN\n",
       "299          NaN\n",
       "300    13450.040\n",
       "Length: 300, dtype: float64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GC_year +  CL_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proper treatment of NaNs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer\n",
       "1       7710.675\n",
       "2       8493.488\n",
       "3       3464.013\n",
       "4       5350.537\n",
       "5       4154.427\n",
       "6      11049.959\n",
       "7       6297.717\n",
       "8      12445.192\n",
       "9       5042.297\n",
       "10      6654.568\n",
       "11      6560.894\n",
       "12      5938.369\n",
       "13      4840.914\n",
       "14      3636.917\n",
       "15      3283.148\n",
       "16     10129.030\n",
       "17      7831.254\n",
       "18      8150.175\n",
       "19      2617.498\n",
       "20      4632.129\n",
       "21     11182.840\n",
       "22      4527.870\n",
       "23      6139.032\n",
       "24      6229.264\n",
       "25      5711.299\n",
       "26      7742.674\n",
       "27      6416.623\n",
       "28      3965.804\n",
       "29      5505.936\n",
       "30      3781.780\n",
       "         ...    \n",
       "271    11093.339\n",
       "272     4133.785\n",
       "273     4271.345\n",
       "274     9694.788\n",
       "275     6333.698\n",
       "276     8637.535\n",
       "277     4711.474\n",
       "278     3231.753\n",
       "279    11464.558\n",
       "280     6182.487\n",
       "281     5923.665\n",
       "282     3389.128\n",
       "283     3438.182\n",
       "284     4461.087\n",
       "285     4956.869\n",
       "286    10382.334\n",
       "287    12452.352\n",
       "288     4570.903\n",
       "289    12316.420\n",
       "290     7815.447\n",
       "291     5746.336\n",
       "292     3259.321\n",
       "293    12764.541\n",
       "294     8265.949\n",
       "295     3136.466\n",
       "296     2844.458\n",
       "297     6890.217\n",
       "298     3333.994\n",
       "299     8359.712\n",
       "300    13450.040\n",
       "Length: 300, dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cons_y = pd.DataFrame({'GCy': GC_year, 'CLy':  CL_year})\n",
    "Cons_y.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "average 6596 kWh: match data note!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6596.342086666654"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cons_y.sum(axis=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfad680860>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFcJCl6RCWOiSVIj/B5ANutyY1pYjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfad756048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cons_y.sum(axis=1).hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Max consumption:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    300.000000\n",
       "mean       7.046480\n",
       "std        2.175902\n",
       "min        2.426000\n",
       "25%        5.609500\n",
       "50%        6.827000\n",
       "75%        8.358000\n",
       "max       15.610000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cons = dfGC.add(dfCL, fill_value=0) \n",
    "Cons.max().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3+11FVyU5cdKhllJVB+i2VL4MHAQeqao/nGyqkU1V1cH++leBqUmGWaY3Ah+ZdIhhJLkA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfad5042b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cons.max().hist(bins=30)\n",
    "plt.xlabel('Max consumption (kW)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Full load equivalent time\n",
    "\n",
    "T = E_year/P_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     300.000000\n",
       "mean      957.708700\n",
       "std       316.432769\n",
       "min       287.244484\n",
       "25%       704.633320\n",
       "50%       937.249319\n",
       "75%      1162.306100\n",
       "max      2368.646331\n",
       "dtype: float64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cons_hours = Cons_y.sum(axis=1)/Cons.max()\n",
    "Cons_hours.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfad073f98>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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80bKJY6lqw/5VdhQean49/kHxOCvpy8X6X5V0QtJ5Sd+WdHvLa76s5q/vryrpL+2SnlZz\nyOB/1RyvfKSbPpFUVzPUfijpr1Rc0JbhsUkf/YOkZUmn1QynyTHvo4+qOZxyWtKp4nEvx1L1D64U\nBYAkGHIBgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABI4v8BHzdT+ySSmC8AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfad1a65f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cons_hours.hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Effect of having controlled load or not\n",
    "\n",
    "consumers with controlled load consume a bit more electricity, but the gap is quite small (14%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_CL = Cons_y['CLy']>0\n",
    "has_CL.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "161"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_empty_CL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6200.501024390227, 7073.679838235281)"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cons_y[~has_CL].sum(axis=1).mean(), Cons_y[has_CL].sum(axis=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1408239125209925"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cons_y[has_CL].sum(axis=1).mean() / Cons_y[~has_CL].sum(axis=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L6ZyIuz9PlCgyyx7MmHbgxlzrhvLNwPm1lN8J3FlHW0uA/N7DLyJ55e7oFq/Cke2URja6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\nE3zQES2AjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf6be58358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hparams = dict(normed=True, histtype='step', lw=2)\n",
    "Cons_y[~has_CL]['GCy'].hist(label='GC no CL', normed=True, alpha=0.3)\n",
    "Cons_y[has_CL]['GCy'].hist(label='GC w. CL', **hparams)\n",
    "Cons_y[has_CL].sum(axis=1).hist(label='GC+CL w. CL', **hparams)\n",
    "plt.xlabel('yearly consumption kWh')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4d) Consumption/Production relationship\n",
    "\n",
    "observations: there a small positive effect of the consumpation on the PV sizing. However the effect varies with quantiles (cf. Statsmodel [quantile regression example](http://www.statsmodels.org/devel/examples/notebooks/generated/quantile_regression.html)). There is a spreading effect.\n",
    "\n",
    "* high quantiles: big PV production comes with bit consumers\n",
    "* low quantiles: small PV production is almost independent of consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average data\n",
    "\n",
    "TODO: add controlled consumption to GC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>QuantReg Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>GG_ym</td>      <th>  Pseudo R-squared:  </th> <td> 0.02678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>             <td>QuantReg</td>     <th>  Bandwidth:         </th> <td> 0.06531</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>          <td>Least Squares</td>  <th>  Sparsity:          </th> <td>  0.2066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Mon, 22 May 2017</td> <th>  No. Observations:  </th>  <td>   300</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>14:42:12</td>     <th>  Df Residuals:      </th>  <td>   298</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  Df Model:          </th>  <td>     1</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.1297</td> <td>    0.015</td> <td>    8.459</td> <td> 0.000</td> <td>    0.100</td> <td>    0.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GC_ym</th>     <td>    0.1016</td> <td>    0.022</td> <td>    4.700</td> <td> 0.000</td> <td>    0.059</td> <td>    0.144</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                         QuantReg Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:                  GG_ym   Pseudo R-squared:              0.02678\n",
       "Model:                       QuantReg   Bandwidth:                     0.06531\n",
       "Method:                 Least Squares   Sparsity:                       0.2066\n",
       "Date:                Mon, 22 May 2017   No. Observations:                  300\n",
       "Time:                        14:42:12   Df Residuals:                      298\n",
       "                                        Df Model:                            1\n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.1297      0.015      8.459      0.000       0.100       0.160\n",
       "GC_ym          0.1016      0.022      4.700      0.000       0.059       0.144\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.DataFrame({\n",
    "    'GC_ym': dfGC.mean(), \n",
    "    'GG_ym': dfGG.mean()})\n",
    "mod = smf.quantreg('GG_ym ~ GC_ym', d)\n",
    "res = mod.fit(q=0.5)\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    0.129720\n",
       "GC_ym        0.101633\n",
       "dtype: float64"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "par = res.params\n",
    "par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0.1, 1.7)\n",
    "y = par.Intercept + x*par.GC_ym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VS7znRKQgVKkMw8g5gozmzAt4rBwBFuESEXlMRD51F+My08kwcpi4PRMROR44CagnIufg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\nxRGTkGNOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf6b8b3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dfGC.mean(), dfGG.mean(), '+', label='data')\n",
    "plt.plot(x, y, '-', lw=3, alpha=0.5, label='LAD regression')\n",
    "ax = plt.gca()\n",
    "plt.legend()\n",
    "ax.set(\n",
    "    xlabel='Average GC consumption (kW)',\n",
    "    ylabel='Average PV production (kW)',\n",
    "    aspect='equal'\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Max data (i.e. system sizing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>QuantReg Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>GG_max</td>      <th>  Pseudo R-squared:  </th> <td> 0.01191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>             <td>QuantReg</td>     <th>  Bandwidth:         </th> <td>  0.4026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>          <td>Least Squares</td>  <th>  Sparsity:          </th> <td>   1.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Mon, 22 May 2017</td> <th>  No. Observations:  </th>  <td>   300</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>14:51:44</td>     <th>  Df Residuals:      </th>  <td>   298</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  Df Model:          </th>  <td>     1</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    1.0623</td> <td>    0.129</td> <td>    8.238</td> <td> 0.000</td> <td>    0.809</td> <td>    1.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GC_max</th>    <td>    0.0407</td> <td>    0.019</td> <td>    2.101</td> <td> 0.036</td> <td>    0.003</td> <td>    0.079</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                         QuantReg Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:                 GG_max   Pseudo R-squared:              0.01191\n",
       "Model:                       QuantReg   Bandwidth:                      0.4026\n",
       "Method:                 Least Squares   Sparsity:                        1.426\n",
       "Date:                Mon, 22 May 2017   No. Observations:                  300\n",
       "Time:                        14:51:44   Df Residuals:                      298\n",
       "                                        Df Model:                            1\n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      1.0623      0.129      8.238      0.000       0.809       1.316\n",
       "GC_max         0.0407      0.019      2.101      0.036       0.003       0.079\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.DataFrame({\n",
    "    'GC_max': dfGC.max(), \n",
    "    'GG_max': dfGG.max()})\n",
    "mod = smf.quantreg('GG_max ~ GC_max', d)\n",
    "res = mod.fit(q=0.5)\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    1.062331\n",
       "GC_max       0.040734\n",
       "dtype: float64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "par = res.params\n",
    "par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(2, 15)\n",
    "y = par.Intercept + x*par.GC_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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he+wGYK6qdgHmuvcLyTQaxjwHOEFVewDrgJ/mOqgkptEwZkTkCOB0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf6b3db908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dfGC.max(), dfGG.max(), '+', label='data')\n",
    "plt.plot(x, y, '-', lw=3, alpha=0.5, label='LAD regression')\n",
    "ax = plt.gca()\n",
    "plt.legend()\n",
    "ax.set(\n",
    "    xlabel='Max GC consumption (kW)',\n",
    "    ylabel='Max PV production (kW)',\n",
    "    aspect='equal'\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4e) Correlation across PV productions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pv_corr = dfGG.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Customer</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.861145</td>\n",
       "      <td>0.865654</td>\n",
       "      <td>0.924266</td>\n",
       "      <td>0.887806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.861145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911912</td>\n",
       "      <td>0.857172</td>\n",
       "      <td>0.956018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.865654</td>\n",
       "      <td>0.911912</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.838399</td>\n",
       "      <td>0.924184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.924266</td>\n",
       "      <td>0.857172</td>\n",
       "      <td>0.838399</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.879999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.887806</td>\n",
       "      <td>0.956018</td>\n",
       "      <td>0.924184</td>\n",
       "      <td>0.879999</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Customer         1         2         3         4         5\n",
       "Customer                                                  \n",
       "1         1.000000  0.861145  0.865654  0.924266  0.887806\n",
       "2         0.861145  1.000000  0.911912  0.857172  0.956018\n",
       "3         0.865654  0.911912  1.000000  0.838399  0.924184\n",
       "4         0.924266  0.857172  0.838399  1.000000  0.879999\n",
       "5         0.887806  0.956018  0.924184  0.879999  1.000000"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_corr.iloc[0:5,0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rU7OUtQ2qQZbPVQ/wy8N38IQzhidV1v0UZXOD21au5p2lw1QCm05o8nhzJwWz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\nGlP2aPpZZrR2kry6645hyKJe+VrvNC8U7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c7yY7l3aQQ5/tnap/kflr6YmDtkKUwiwI+j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\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KjTt5inNdTFsH4EThiBuHJWK6SxBKjC2NhxaFi6vrxFGlgIIx4tu7y2RTY0xbZzo1ImNY\njFwdQ/HQZZ/bnTy66hPXXBxfmfy3PUhSSI8oJ4XRojbMEoYSSd3BUDz2+xmCUKKUHrKY7LA/zqDK\nAU0ziSIJb/1KtsWrO0uszBxR62WZz3VZyx7w3Bcf4txHbgCQVm2mjSF/dPMcvqeQTo3JJcbEVZf9\nfgbT0knGbUZjg/vnq1yszfPAfJVb3WmCUCJt2Owd5lkstbE9lc4ozlyuz0x8wMtvnWJ6qU27lySb\nNlnNH+H4CvujDIbi83BxB4CL7QVaowSKHHKhVGUh1uEPb58nlxiT1cV74/ZhgXsqderDLCNb53i+\nTWMo3lPnCnW+XV1mYarLwSDNQ7O7fHP3BNZQ5+Si6L0qckDjc8dI/bBgBGQMi+BHHWa+6HC1Veao\nlebk/AGl+IC+E+donGQx3eGdZpnBUZJn7n2Hd9qz9MZiA5vNCGG/6yuUMwLI3R4nyBoWUzGTO4Mc\nh500N5/4be559ac5X67x6u3jKKpPKmGR1F0+NHOLbx6cIAglNMVHl31u7s1w/4ldglBis1Xk3pk6\nGc3i6zurnJ5pcDQWE39D8TBUjzu9LAnd5ZWn/8V7O6iKlcPHFn/2Xd3nuc339jn8ddb7YlNdO2eE\nv/pHJynIQiay4cyyrB/y2dYj/Ej+dZFprh8wpzj8RvtRxr7GhdTuJBnVCjUqSk/QeCJ7pECdCc9z\nSRnw8miVj6bfiUpWn99rP8rfL3wbgKqXo6gMeM08wYLe4tDLsO/keCbzNtUIa1ZU++w40+QUczJo\nSctjKmp/Es+ck01eNldZj9UEVDnqrugIDaAAMgsNoxnhBEFQ8JOyLVIFgjgLaps3rSXOGdVJiVpW\n+ny+f4GUYnHkpvlU9k1aQRI3VMXjjlZ5KiX4sP1IJ3tW3+cgorHfRfVV3QJzagf9uwTal6xFzsaq\nvGEuM6+3WdEOcVAwA4MLRps/Gq6ypDWpqH0+379P4BD9OB9OX2fHmZ5MnJOSM4ksPnCzPJbY5PO9\n+3k8dRMr1EjKNt/on+ZjmXfYdko8Et9mwynjI3NxeIwfm3qdrw7PAJCQHS7Ed9hz8xNouYCceIKP\noHapuiL+5ZjaYdebQpM8BkF8whxY1Q9xQ5m07NL2BW3sirXAqtEgIdu0/RQNN8vLvRX+zvRFNMnj\nleEqXTfBLxZfpB2Vzhv2HLrksawfMghiVB3xuC0/xUudE/xi+UWhRY70owduFk32JvbcQ1+wC5a0\nNn8yOMt98R3+t1s/xD9Z/ir/x/ZH+R9WvsyS2qIfGvzcH/83/K8f+0Nyioku+bR84X6aU3tcsubZ\nc6Z5NLlJw8tNKphyBIfZcacpKMOJsP9fHTzFf116kT0vz6LapuELZOX/fOWTvPPI7/JlU5tgFBcU\nm7edAp+pf4gfn3mDb/ZP8Ympt/CR+L92P8ovLnyTpGwLrW8UB79m7AuzgzJkw54lrVis6o0Jj/fR\npd33flNd+Jl3dZ/nbv3qX/ocJEn6fuDXAAX4v8Mw/Gd/7nYpuv0HABP4uTAML/5lj/m+6Kl6oRLx\nTNOkZYe8MiQnW5xLVvEjalXNy7HtJTiX2GM9UeeyuSCQeWqPG9YcDjJ5xeRBo0c+srYWlRFuqDKn\nDsirQwpRMmtRtjmbuENS8khGrMmc7HBMb3JtXMEMdE7EDigrNut6g4raISPZfDAupu8Z2SImO1ww\nupMk17vupUFkOthySuRki4oyJCG7vDY6QUzy+IHkbRpelkvWIutaL4Io9ymrPTbtMjuReeCcUUWW\nAu4zRoK+JQUcN5p8Mn2ZWV1Yb78zWpnwTmOyS1EZU/OmKEXE+CvOLOv6gCfiLVa1Ho8aY3KKSd2b\nmvQQY5IbYfl6zGodzho1+kGMjGQTk1zqnkpZ7aJLPltugUX9iGXjkDPxO/iINsSS2pk8DydUOKPX\n+XTmMgnZ5f7kbfTIbaTh8yO5NwiQScg2h36Kotrn48k7uKHCjDJmMULefTx9hZtOmS17BidUKCoj\ncrJNzctNXEY3rFk+EjfZdKcnoYOjwCCvDKm6+YkffcMpsqxZzChD5Chjqu5OISOGSXnd7nla3gAA\nIABJREFUFGkAksvPT73CL0x/k2SUkgDwSHybpxLbmIGBhs+asc+qfiD+7pqFLAVsuUWWIwrVjNbj\nZ7OX2fwuzuii2uGKXUGWAla0Dg8Xd4SzbJCckMzKyohQDXk8LrTAW06JnDKiqAyIST6KFPKh1IbI\nTzMrPJW4Q1oeMwji5GWHnDJiFIo+Z1FxyGkmCdllTT8gLTuUlR5Vp8D5co0vmxrflxCDuR13mm0v\nQUK2OZ5sTcIDY5IwzkzFTJ6KN8grQxp+hgXV5MHYHg0vy3pE4hIto/HEEfm96qm+l9P/KObpN4Bn\ngHXg098VFXV3PYMg7a0ioPr/+q96iu+bnupVZ46cMuKqU+b6uIIV13ihvcbcTIe2n6KkDvCReX24\nzJGTYi3Z4KZTjlw/NknJ45I9T1PtUlRG7Hl52l6KXpSxtDGe5YnEJgEy7UDna+3TnDGEr3zHmSav\nDHmxv8ZyvMmONR1FI+9zxZ7nwM3yaHKTy+MKN61ZFo0WPT9OQR6JwUYUJBiTRV9y0y5HvNVp5lSR\nC3Ui1uCKPc9VO6CkCpfVppdi0y5zxZynYnSYUXvEEHbBm1HEtBseMgjibLoG3xksc8fJs2UWWdDa\nVPQOm3aZOa3Dnp1nIzZNTHY4pvp8y8xxb3yXa056clKtSw6vDE9wLlGdBPu1/BRXzQqZjMVNa5aY\n7JKTTaxQZcedZlVv8Ja5xInYAUtak+e650gqNl03wSOZLXEqk0SuUVHtC0mNn+GFUYVl45BLo2Os\nxesceRlmtB7P987y9/Kv8LX+OovZNq+Pj1NzeyLW2y3wncEKARK3jDLT2gBD8th2SrihOgkWvCuH\nckOFb1o6BWXIi6M1ptU+bT/FgSvYn41IOiXcTCobThkzGloW1CFNL8MdJ8/FowXOJ/eQCfhy/yxt\nJ8nPFb+FFWq03RTftGfIKmPmtA6jQKfnJ1k2hJPo7aMKH8qKYMANp8hNa5ahb9D2kzyW2GTDEUOw\nTbvMqtHgqikITl/eW2Mh1iYRs9lzC4wUg4YfICU8vmaeJCnbFNU+b5vHuCG7nDAa3HHyDHwRgZJV\nx3zFXGQUGBFKUeaaNU9WGVFSB3QDlTdbi/xE/jXMwBCoS1m8t169fZxfLH+Db4xlnowHPDsS6pNN\np8RrzSUWYm12hnn20nlGoY4qBbxs56m5eRa1FtecAntugUWtxRVnFjeKVwdoRxjIbvA9cFS99z3V\nh4BbYRhuA0iS9FlEHNS17/qeTwK/E4qS/lVJknKSJM1GMVN/4XpfbKqG5OFGp9UGGU7F9glCmXKs\nT4BMURUWQ00S5U5WG3NPvEpRERnjMdmlFmWXjwIDN1RZ0oS0JacI5uOsLmDDRUXYUSvxLqNQ9CLv\nyntW4wcs6UcU1T63rDItPxn5nZ0JQ2BG61NS+yxqLbbcEhW1Q1oZIxOwpDXR0h4lRWyaW26Rpp9G\nkzyKiknXT7DtlPhgfIc5tcemUxJSnKQgc90fq9IKxGS65yeZ0bo0/cwkq+pYrIUZ6KwmDgV8RRVW\nUk3yWYk18UOZs/oh37JKrEbx28IeKgTrzSDD46mbKFIwabU4KHSNxORDMPDjrOkHtCMYuBbFGlci\nkHFRH+CHMjNGnzNGTcBbkFnQWhOv+2V7gSM3xalYHT8ibiVlh6RsMw50Gn6GB1PbyFLATGRVrY+z\nOBmFU4kGVqgy9GOU1D5rRp0Ne26S3qlIASv6ATl5TCwlknHntA4pxWJJP2KFw0nuvSi5j4STy89G\nQ7+QJb1JTh6TkEQy7lZmegJqfsMX8TmjwJjwe2VK+EhcMOpU/RTXLIOGm6OoDljOtljSmuRkm0v2\nPPcldqm7Uxy4mUi6Z06ipdf1Flc0EW6oKT6rRoPV/BEr+gFlZchVp0w4VqOE3y4KIXrc59DLoEs+\nDye2+HL/HtaMfU7HarT9FEtakwVVvLdXjQYLqqhiGn6Ke6b2GQQxtpwZyqqApK/H7qCovkj59aZ5\nduTyiaTJZUcM9EqJAcv6IQ/nEyxqbRpelp1+ntpUXrSo1C473hQKwYR4tqC1cENFaJXxWYogRu/9\nCiF81xP9aUmS3viu//9MGIafif5dAarfddsd4OE/d/+/6HsqRLl9f9F6X5T/ARILWiuKLBFOmbvI\nvbwyRInkKyc0lYTioCCAvm0/RUW9S7DvCX94FEgHcFI7FNEe0eZQifSoRWVEEEosqH2WIoF7UTZF\njnoE7HBDhYrap6x1OWXUKSlDimqfGa07gYksaC3WdVF2ntQPBPTYT0426yXtiLLSZ0VrEYQya3qT\nojqgFwF9y2o3ArGoBJHzqesnqChDocvVDwSZK9RY0wXPdVbrYkentrvRGUEoYwY6i2qHdqCTU0xG\ngcGqOqSidqhobebUTpSsIODKd3u1d+HQc2qHRUMQ7M1AIx9djO7RwklE9YJqkpAdZrQ+QShF5b5K\nUelPoN1WKIAps3qPitIjr46wQm3SP86ppihZ/ThlZURGsahoHRbiHRbVzgSwUtL6nNIOaXoZTurC\nXbWodlhSe3QjqI4TKsxpHZbVISuR8H4QXZQAltRe9J4aU1b6LGhCh6sQYoXiPGEGBkV9iBkaArmo\n98lrowkLVZd85vQOc1qHup+YwFRyiokTqkwbw8nvV1E7DIKYaGVofZY1i3W9hRMqUXKujEJIWe2S\nidn4ocyBmWYU9dcX1TaSI03+fj4Sfiizoh9wSmtx6KeZ1oYRYUyEL1qhIIMtqL2JRM4NZeaUAbIU\nMKcMxMVJb5KRLdxQJZWwWFBsKmqHkjKYyK10yY+A3SpDXzx+XhmSMSzKapcV7VD8nkqPnGIKxJ/a\nRyHACVW6fpIlrUvDT2IG36Pz2rsv/4/CMHzgu74+81c9xP/X9b7YVENE4mhOdjBDlQ/GdwhCmWem\n3qbuTpFXhhGLM0FJ6/MT+de4Yc1yTO2gEPLyaJW37QpmYAhIrnWMsiK4khfHS1H0xSGvW8eISR5V\nL8ffmXqDth+j6cdZ1Vpcc8p8JHFzAgT+cPoam26RXATruOmWKCkDKmqH80adObVHEMpUPY3zRhUH\nhbzssO/kWNeOJsSspp8WRHVviqqXoax26YcGV6wFFELOG13OGDU+mtjmpiN0ha9ax8grQ8xAw0Fh\nSe3Q9HXuTeySkG2RTiA7vGMtUFb6HFP7TKsDoXeMdJ4LWov/ODw9oTG1o2FJRrbERqEKJuuy1ufj\nqauCv2lNY4UaCdnl1fEyDxhDLjsKH4xvc0Lr89zoJKdjNXp+nO/PXqasDDlj3OGqPc89+oATmkVR\nGVFQhsJ1FmmGlzRx0r1Xb3Ey3mDTKVNQh3zLXOEBo0EuuqDdTUh4OvUOS3qTLw7PcsueoRsk8JEZ\nhRrPjU5TUTuUFZt3IkTfV81l4QSL7LcfjNWouXkafoKibPOl0Qm60Wa7bBySV0yBklQHrBs1quMp\nTmmH3KO3OBXb52zyDm0/Rl4xOaGJzTgjW8Qkjzm1hxXq5JQRa0adO2YuujCL/vSavs+yccgxvcmX\nRsepeglWtY5oAbjTnDAa1Lwp7pna54JxyEEvPTFAyFIAitCdrmpH1NwpzhoiKmcQWT/ntA5lZciu\nM01MclmJsrsu2gtcMBqTC0bdT1PShfVU9F21SZpuUnd52yngI7PhzLLj5nnONPj+hM2HCze4EHGJ\nT2m9Scvgbp81KXmMQg1FCkjIwia9qo15KHabJa1JNZKMfc82iiB8d19/+frrRD+963io90X53/ZS\n/MngHC0nxZzRJaHY1OwpHk9v8jv1R0moDgVjRMtOUjBE2fq7Ww+QPWmyZxf44u4ZiqkRDxT2GPsa\nrzcXaS8Ie+uLR6sc5DKMPINLrQoPF3c4sDP849mv8P80P4QihcwZXS52F/DL3+G3dj7A8UwbLe/x\nbPM8D+Z2kaWAV9rLPJDbY1obcBU4dDM07CxFfSDaD3aOitFly5zmS9ppnm+e4Vy2xsZghvPZO/yn\nvXN8rLKBJvvUrRzXOjNslUqsxA5JKxbdYMh/OHiAgWOQ1m1OpQ8o6f0JyEWRAlKKRdXKU9BGXB3P\n88W9M5jHdHpenF0zz+1Ukb3xFJavcTrd4Mv1NXZmpjFkj5qVQ5UCzqf3+HrrFBeyVQ7dNCuxJgt6\ni88ePsRWZ5pWKcnr6jIX2wuYszpXhxXuT+/ghiq/t/cAHy5v8k5/jnvid7gY5c2/0TkGwL6Tww0V\nxr5GQRvR8+PUxjmelc5jyC6XZIftcZE3W4s8XNzhteYSZsVg4Mf4wyv3oZ0Trq5ifMA74wU+X70X\nRQ44lp5n2hhiyB6vNZe4np+jqA/4k9o6nXKC15pL/Nj8RQ7cDAd2hm42we/feZDTuUXmjC5f3j/N\nSvaItWSDrzdPcia7z76VZSHR4VyiSstK8vxonZxiRl57hTcGx8lpwj2WVUXMz76TpaT3eb27xFay\nxKGdxvI1/qB3P5tmicV4m46bYEozkQn5+sFJzhfuUNIGaLLHS60TeKGMLnsUY0NeMJfwr6f5yto6\nQ1+I9mdflHjxkTVuGD2+tH8P1ryOHWi0vSR9L4Yhe7wznuf5O6d5ej7JtDagZk/xRmsRZTHg1eEK\nY18TOEN9yNcPT3IhX2VKNel4Ca73y3xo5hafqX+I48kWrzWXKCUGyFLIVuEGv5yr8uudMzSsDC+Y\nS1wdz3P7qMAXsvcxrQ3ZtQrktRF9L8Yf+/cy8nR209PkVXFqrlp52m6SWaPHn62a36P13qqVXgdW\no9inGvCTwE/9ue95FviHUb/1YaD3l/VT4X2yqeaUER9KbUzo5HOKyaYh3FM/OfsdkrLDgtai7k1x\n6GVYM+r8/dVX+GDiFjX9EP+YzHGjyZqxL1oH8SZPJm4wijKDHkhss+NMcyrR4EL8Nk1fOGw+lRfK\niFWtJe6vH/BTi68Tk1yW9CM+Ubwk8q5CmZmSaCPcPeUBkwBBhZBuPEFZEYOaJxM3Wao0iUku5xJ7\nVNQO2ePjSZ8znR5zKX2MVb3BgtqjFxjkFYtPli5NJF9lrTsBsxSVAUEo0wqSFNUBMgFnjDqJJYdH\nk5skJYdL8UWRi5VWojJMBOPdF9/BDA3OJQSTNCa5FGf6rGhNFEJkKSQteVjTb/F67DgPp7dYVNus\nxA75ZOoGV4waZXWARoC7oDAdtVBysslTyQ0Ggc6UOuLJxCYkYBBoNH3hcMvJJi/Ka3xf+gptPyVg\nKMDyfJOKJtoNH0xsUveyfGRtmifT1wHhWjoV2+fj8wqG7LKgtUlHdtdpbcgTyQ3SkVXyA6mbLBpt\n7o3vosQDGl6Os8Y+3bkE60aNkjIkq4zFlF8ZMq0OeDC+gxkIhmorMCjERjyZuEFWdnk9qnjui5Qe\nOgENP4MTKjyR3JhYQ5f0I7SMx2/UnuLp9BUeSCRRpIC0bFH3pnBChXxlxHrsDnPKgAM/xUK5PRlo\nfrN3ksfjO2hn+jyVvD4JN/y9DzzGE5kNFtUO+fkRj8Rv04wiTPpBLOqjHhFbdCdJq35CYiV2yL1G\nLepvCjfgK8MT/PDs2ywbB9HwUePJzHX++dYz/IOlb5BThK1aJAGrXDDq/HrnDP9oapcbZpnH4zuU\n1R7fyR3jycx1kpLDh1PXhFvOy0zSG56IbwvXYhDnfGxvImv859+LzeI93FTDMPQkSfqHwPMISdW/\nCcPwqiRJ/210+28CX0LIqW4hJFU//1f93PeFTnXhnkz4+G/9JKXYIOqXKox9HVkKqJnClng60yAI\nJXbNPEPXYCHZ5WSywc1RGUP26HsGed0kCCUO7TT3ZaocuSlWYodcHi0Qlx0O7AwZzUKXPZxAIN40\n2ScuOyKHR+9z5KY5l6jy1c46eW3EONDRoyz3kW+QVi1GnsHpZJ3XesdZSRxxdTCL6ek8NX2Dtpdk\n7GukVJvbZoEglJk2huyNpriQq3K5X2Eh3qGgjbgdnULvT+7w7f5JTifrXB7OU9SH3ByWmNJFyTXy\nDPL6iJI+4MhNMa0NGfgxBl6MOaPL7rhAzcyynGqhyj57oymKsSGKFEZYNoOcNqbtJPmZ0ks827mA\nIbsMfYOCNqLvxRn7GjnNpO0mSasWQSijyj5BKOGFCjnVpO+JEvp0ss6lwSInEodcHc6yGBd+/aaT\nxg5UOo74vrl4D0P2sANhzR0HOjvDPJVEj2PxFnU7R041GfoGX/jOffzgg29zOlmn4yXZMotUYl2m\ntQEvHK2RUm2WEi18ZHbNPJVYl4fTW3ytu04QysQVh4I24p74Hb41OMmi0ebWuERRH9Dz4vTcOFlt\nzLfqy3zq2GXeGcyxEO8wpQpOw6uDE+RUkx2zgBMolGJDVMnHDlS8QGHaGJJWLAa+KG13zTxBKPHJ\n4iUumwvk1REbozJp1WI53uTlzgoL8Q6qHNB14xS0ESW9zzdbq8wnunyrtsIPL13mP2yf55PHrzCt\nDbgymOfivzvHoz97ETdQiCsOC7E2DVvoSytGlzd6x4grLnl9RNdNkFHHyFLIkZNiKd6KggcV6naW\nTxde44+6F6iPs5xMHbJjFsjrIy4eLXCuILgJO8M8D+d3GPoiGLBhZZjWR/zLymv8L80z3BjO8Prt\nY9y/tMe0MSKpCMi7j4wdqKQUG0UKaDppziRr1OwpSnqfmOTyy6dffG91qnopfKz4E+/qPs/V/+V/\nmeL/M+f08DefXaCoiN7PXc+1SLLMkZbHrGgtGn6KqltgWT/kldEqn86+RdVL8LK5yoLWjmJUJKpu\ngacS29S9uNCDxoTY/tBLc3+sSi0COd+dtC+rQ96wy6xqTV4dHycmuxHKLUtBGU6u/KL3F7CsDjny\nNVpBIpoiexz4KY6pfb44PMMPpK6y6RYmGsKy0mfDKVNWezihQkkZctmuCMqWYjIIVU6oMl+3hDRL\n0P8d0rIVDZEGmIFKN4iLOBZlxJp2xNfMk9wf2yEvO7w4XmbdqE1OtHdvXzPqf0YzmIhMAOUoiUBD\npGZuuNO8MVrmvsQOa3qTl8fHJ69hURkzq+j8/mCRstbljdEyT6evcEqz2fUU3rSO8XRSWH7tEKpe\nhn4Qo6AM+fboFJ9Ivy2GKYrNfxyeJi2PJ5Kmx+K3qXtpfr/1MD8z/RIgBmKX7BLbTkkMGCMNZExy\nuWIt8FhiiznF53eizKw9N89J/YCsbFP1sqzrwvK5rB9SUYa8NF6ionVISg477jRrulAYJCL+6j/f\nf5p/OvclcrLMV8azDPw4q0Zj8hrtuDkCZI6pnT+TUQXwmcYT/Mr8F+gFGqOIKQFiYCbUB00WVJOd\nqK1wN/Hguc5ZfmXuy3z0zV/g8/f9lmAK+HH+3h//Er/+zG+zph1xyZ7jglGn7ieISR6DIEbLT3He\nqPOV0SmW9UNWtQ4B8Kp1jI8l9qh7KpoU0PBTvGEuM6P1mFM7FJQRVqjS9DP82s5H+J+OP0dMctlz\n8yxGUT+ntB4vmEs8Ht/hd7oP80+LV/mmBf9i7xn+8fxXySkmOdkRgHA/wyntkCvOLPcbteikrdIK\nElGkypjTi/vv7aaqlcLHpn/sXd3nuca/+i8T/WeHKmXF5Io9S0522HKLFJWRoLIrI9p+iquOAA0v\n64dsOyW2x9M0fIM5xWTox8hHTpK72Luql4igFCJK95pVISYLOn9BGXHFnqMomxRlk3ccsQFedcps\njGe5alYmFPMFVUhicrLJnCpO0jfcLJftiqACRQOItGyx6U6xZ+dpBQbXrAplRRB/ErLHdUtwCcRm\nLjieZcWk7ie4ZM3zhqOzaYvUzaRsU5BHJCWHGWUYZUUF7Ll50vKYbXuGhp9gczxD009zzZ2Opuk2\nh36astLnVesYuuRRjsTjOdkUEjRnmivWAm0/xqZboBmBagryCE3yKSkDGn6ShGyz7WaQpYCrTonv\n2DE0yaPmioypQz8ttIt+lpjssu2luOFmafgJZqI48bTsoEk+B36Khpflhpuhoglu6Y5TFPphp0hO\nHlPUhxSVMTtukYt2mYraxQ40dqxpAOaUAbkoCmfTKXHRznFrXCIfOY/EADLLtlPilpvhljVDWRnR\n8EX8dlEZUVTGOKHCZbvCllti0ynR8FN4gcKBH+eKm6CkDIQZQxnR8JPRcHFAMSrhG5HueU4dcNMp\nY3o6dT/Bm9YxVrUxm06ZHbdIRe1MVCt3kxt2nCK3LMGsLeoDrjgZLEtjx8tS9YSoPtSF3K3px6m6\ned52yiQll0TkmMopI6pehmvmHGV1QNVPUY3eMxtukj1vih1viqIy4uZohoRsU1GFOiMvW9ywZtFl\nP5rsmxTUIQ0vix/KbHspro7n2XKnuDGcmSQIpDSbBbVHTPK55sxQVMYUlT5XnNnocadFKkLk2Npx\ni1SjiKD3fP0tQP+9L06q6VPl8GP/5lO0rCSF2Ii9/hTl1GDio/Z8hUzMQpUCenaMsaMxHMQ4vdAg\nprhsNGdYyrfxApmY4jH2NBQ5IAglDgZpZjN9dttTHMt3kKWQmOJyZ5BjPi30dC0rSVx1aY8TjGwd\na6wzW+hRSgyEdMg1UOSAhOrQc+LIhByZCU4XDjE9DdMT2fF+ILPbmmK52KIxSLM81cILFHTFY+Oo\nRDE1IquPGbgx9nsZzpdrdJ04R2aSXGxMe5xAV3ySmkNat7B8jZRmY3o6quRzZ5Ajpnp0xzHmsz12\nWnmyyTEZ3abez3Bq+pCjcYqVzBFX22VUOWA+3cULZLxAIaE6bHaKGKpHLjZm7GnEVZeRq5PWbXY7\nU8xm+uiKz8gVou+kZtOxExiKx9AxGFgGCcNBkULU6DV2fIV8XLQqVFnwBTp2AidQqHZzHM+3GTgG\ncdWlb8fIxcbstPPkEmNUOWA6PuTaQZm10gF3Bjk0xSdrWDQGaSxHI58ymY6PsHyVni3K76xhsXOU\n50LlDnuDKTKGhe2rdMw4xeSI28085yp1TE9n6BgspjuYnkZ9mEWSQhRJvO8N1WOnUeDMwj5BKOH6\nCmNPo5QYYPkaQSjhBzKa4uMFMqocRD+vzc1OiYPaFGdW79Cx4iyku1w9FK97Jdujb8eIq+7kfT5y\ndfqWwWx6wPbBNEszLXZfm+fEo7sTjsTVr5zk+Id3UOWA3c4UqZhN1rBIqA57/SkWMx0sX2OrOc2p\n0iFORCaTpZAglMRjai7F2JDXbi+xPHvEYrLDgZVGlQK22gWG3TgnFw+YipmoUsBOP0/GEC7E20cF\nyrk+ewd5Ts83SGk2nz3+Aj95+ylMT2fsaWT1MZavMXL1yWOrUoAXSQ0HtkE+bvKVD//ae39Szf/I\nu7rPc4e/+Z/9pPq+GFQV9QH/+8KzvG1XWDf22XRKrOqHXLQWRfa7myMpC6H2wBf+/q93T/OzxZc4\npvb5t5mH+XDqugBG6w5v2cnIK+7xwug0T6eu8tXiOg9EVsMFtcsle54LRpUAiU23OHE+vdg/RUqx\nuT+5w7reQJFC2r5I8lxWh+x6CQZBnEMvzVOJHY58ja3oZNIPYjybucCnC6/wsrnKx1NXCCJ95u8m\nH+Yjmat8KObw+VGeS/lj/KPCy2x7CTadMgtai1dGq2iSz3HjkIoq+pSnNI+37CR5xeT18RL3x3Yn\nxKPPpR7mmam38UOZ61aFH828xbeiNsBGbpZBEOfH0hvIQDuAnAxfyK9ghRpnY1W6vjiZdYMEx9QO\nf1K4hw8kb9D2UyRkexJed+inxek/sl2CkNj4SCIcULZJyDZWxLKtqH2KSkDV03h1vMKKfhhRrEYU\nlREbzgzNUoacYlJQhjxgtPnV1OP8V/mX+Ia5Sk4Z8Visxr/tPkDDzvLR3FVWtSZuKPPqeJmcYpJW\nxjybuo9/Nvs1/tNoifNGlYafYdMuczZW5evTp/nx7Bt0A4MNe44fSm3R8iVetxYJkPFDefK8/13s\nUf7B7NfxkVhWewxCFY2AXW9qkg6akHzejqhXInGgxTeyJ3k+eYb/fv7LbNizPJ28wZemTqMQ8MnU\nDZ43l1nVG8JGqgx5fbxMz0vwycwlfr/wEB9KbfBL9b/LLy+8gIbPitbhowv/hP9z+d9T99K8ll/h\nhHHAitbEkHy+OjrN2ZjgQTybvI9fKb/A63YWK9QoKQOsUKPhicphXT/gV9Xv46enX2FGGWJGYX7P\n585ypT/Hp2e+w1Pxhqg2pgRTNaeYfCF7H09mrvPb8cf5hfI3WVB7/OTtj/PZ4y/wpi3aJ+eNOlao\ncMmeZ0U7FODqKNUiLYvT+pLe5Cvv+U7x15JJ/Y2v90X5LxOSlIVOb0ER6ZgxyaeoiqC2u+LznCw+\nTDnFxA9F+WKFMinFEjY8yeMdR2hAk5LLnOJQjCb1d3F3CdmOACp97FDBDWUSkj2BUYh8nTEFZcg1\np4xGyDFVRFUf+UKZcFf8veslmFM9FClgXhXPK6OOSUYg7KQcUPVyHFcV7EBlQe1z2RFQYlkKqft6\npJfskZYtErIjtIoIEX1RtrnuiGTPBSUQ8cJ+iqI6ICOJ3mhSciLNoIOM6JmK1oVwN9U9lRtunLqX\n5pqTpqKJsvRP7+eSlsfU/TSDiAN6t3cZk1yysk1BGZKRLczAoKx1OfLSE4BMUe2z5xaIRWmcuuQz\nCHR2PH2SjiteL9Fe2Han6fpJCspQwLWVIXVfnLbcUGZRa1FSBhz4QlWgyj5zagcfiXTE/tQkkVIb\nV1y2PXEumFM90vJ4ohKQCXFDWfwtpIBrTpqkHEy4A0SvVVntMW2MIquzQ91P0PDS2BF0WZM8ZELa\n0YaRj573KFQFbDreIyeLYZEbaYLTisVuNNQrR/rOuWjAo0gBV50yM5pA5IWhREEekYl4FdJYpunH\nSUStDgFT8SgoITNaF1kK0CThwLvhGhPH1mV7gVz0+8sEVL0MM0afDXuWXmBwTHVJyCIzLAgFKHvT\n06i5wilVUgbo+ExrQ5KSw7QxIqeYuMiYns6btsP9hh69NxUafgqFYOJeK8hCn1xWB+QUc+Kue09X\nCGEYvKuvv4n1vthUDcnjdWuOj8X3ed0WXvJNtzABhsypHZ5J1knLDq8MV9l1ivx8NlkYAAAgAElE\nQVRs6SXMQGPXmxJQD/2Imp9lEMQ5G6tiSCKnyglV6p7o9ZyNVclIghr/jrWAFapYocqq1qESObl+\nKv8KJ4wDNuxZimqf16wFnjeXKShDrjpzfMU8OSHZC0++OIW8bFXIyQ6LRgsHmU+mL3MlEmi/Ysf5\n74rfZNvNc8WeZ07t8YnsRbpBnJj0p+6mDyRvcCGSsYiIi0KU1OpxxU2w6xR5a7xE2xOotU8XXgHg\nwYjYv+tlWNMP0CWfBa3FGaPGoZ+i4eUmjzEKDB5LbAFwr35EEEp0AwEmVqSApp+hGAnbi8oYF5mY\n5FFUxlyI3+aGNYcZ6FyyFllQe5NhXlG2uWB0ySsme16eb49OUffT3HGm0KOo76Js8srwBBWtLcAr\nRg03VLhiVxh4MRp+ij23wJ6b58XRGkW1z5OZjahPmWfTLbAeyYbu1Yd8MH2Thp9hVW/wgjlPTPKi\n5FCHB5LbE8jyqt5AlgKuOYXIyrvPD6W2qKhddtxp2k4CN1TIyjZfHpzli93zjEKNZDRwecE8xSVr\nkbIyjNxrBjvuNEW1T9MSf4ufSm+z4RSpaB1GgcG3zJOs6fvseFmWtTbfHi/wZGKTe+JVMrLFZ6v3\nk5MdVirNqL9pCQCLAptOmSCUuRDfYUYZ8hVzkQ03SVK2+fbwFEVlxE8XXmbHncYNFZa0LmtGfeJ2\nWtObJCWHoW+wZuyz5+W5aOfYdqd5On2FzVaRsiI4CSIp9g6JCMizaxXEBUaxyckO2+40Y09jx53m\nD4cZfiTVp+GnqLlTfDRxZ3K6P/TTjEKdupdlVW8wE0F93vP13or/vyfrfbGpuqHMut7gNXuKlUgX\nuqYdsWIckJEtmn6GV6wcQSjxUFJkEb0yWgXgYaODFWgkJYez+hHruthg7FBhXT9gUWtxwWhPrKXd\nQEyzZ7Qey5rFCc1i28uSly0UKeBlc5VBEKOoDijKJk/Eq1yI7VFURjwS2+Wh2O1JlPWq1uKYalJ3\np1hU25jBnxLP37QrrGotisqIJbXHH/Tv5Zja4eGYgG1fsRY4pfWwQoWEJErtTafMpl2mII85Y9xh\nQe2yrndo+UkWlCEzWpePpq6SVUzcUOZlcxVZCrho55GlgFNan6qXiyhNBaxQ47zRZV1vsKq1WNVa\n5JQRV2xBq7/mTE1K+DXtiNOxOqt6Y6KLbPpxNAIRfewLV9Oc1uH+xG0WtDZ1P40u+VQie+w7Tpq2\nn2BFa/JU8jrLao/7ErsT2+6Bn+LD6eto+CQjdQPAYzGR73VBF5bKgjrk6dRVglBm056hqPQ5ox+y\nrgtnU9Ut8LaTourmOasf4YYqZ406VqgyCOI0/SQ1N88JzYqqEZWKMuQeXdh0t7083xrP0vTTlJQB\n59I13FClGST4wcwlPjl1kVPamFaQoOnH+aHUdT6WvEXTT+KgsGo0WNGaIiI708AMNF4Y53nQOBSE\nL3nMx5LXcFBYUPvsuDkejFXZcIrcsOYoK30+MLNN049T72YiUr7KPXoLyZF4LH5b0PfdaQ78FBeM\nKsvqECvQeSy5ScNL8+LwNA/HqrihSt1Lo+NjhUIhUvUyFJUx05oY3q5oTVa1DnNqh0vWIvfO1BmF\n2uSzsuNNEYQSDV+kBw+CWBStHWddPyCrjzlv1FnTD/jaWOEjcZ/7Y1Veswus6QdU/RR6xFVYULs0\n/TS9vwWUqu/Vel/0VN3IVldzpyYypJrvEiCwZG0vhRVok1ZA209StaZoJZJsez6b4xkW9BZuOCIb\nlb8jRScXir5f1dO44+QnLNSFSMC+6cZRCGl6GQqyKXqlTgZTMZjXW9ihwoGvTGQiacWjG4i8eTkS\neoMI0pOlgIxsse9kaRhZDtwcdUVkSrWCOFUrTz2epiCb1Nwp9pwCQfIWtYi4NPBFbMltuzgJghsE\ncTStTd2bmtgF3VBBkQJqXo6qlWc9smR2vCR1X6fm5gUpKjqdr+stzFDDJcANZfFauznBHvXjIool\nipVRIhxgzZuioAwZBQYD2ablp0T8TOR9vxC/zZYjICNtL0VR6dP103SjjVeE9FlUI2tsw8tNeqqV\nqHfdim6LSa5QQZhZtj1Bn49JMjHJZ9eZRpYC6t4UihSQk8dsuSW6vjhZuqHCKJDZckrIRoO6O0XL\nT+GrMltWiVtGlVaQpOlloiwuhSM3HcVmx5jReuQi2dTdvnFF7aJJPrfc2IRZqhAyF7WAYrLLKe2Q\nup+m7ojNqOaJx62qPcGk8FKs6gdsOyUCTaQb3L0Y1OwcrXgCLWKlBoEsXt9Qx2GA7EqT99TNcZm0\nbKFJHr0gxAo1nFCh4WVpOBlcJA79NG6oTmAzVqAzCGKUo5PiIIihEAo5V5DkjpMno1lsOSXy8h57\nbgEFcT9FCuh7QtjvIxB+AJavTUr+mjvFVaXKGT3OS+M8q1qLhpfFDdWI7+rR9DK4yvdgawlD+BuK\nSHk3631xUk3IDk0vw89kanSDBGl5TNtPcX08R1q2OBWr83czVZa0I14ZniCrjPlU/iJJyaHm5VhP\n1EnLFg0/yw23RF4ZUlF6wm/vFKh5OWa0HgVlyJq+Ty3Cw/mIfKB1Y1/kAhHwI7k3OGYc0fZTtIKE\nkN84MwyCOC+YJwTkWBfDg4aXjTYZYeNcULssGm0yssUPpK6KjKsghhkY/NL0i4wCg1fHy5wzanwk\nfZVrTmFCQsopI84Yd/hw+jq65LNtzzAKDDbdAmv6Plagcccp8PzgLHVniora5Ufzr6MQ8rHEzehk\nkeb+2A4AD8a3OWPUuOJMs+nMUPVy1KJolKeS11lU2zwW36Ygj0jLYxp+hrfMY2w7Jc5GSMTzRp2i\nMqCsdjlr7LNqCH3ns70L7LtTnNEb5NVhBL7u8Uyyzhm9Ts2d4quDexgEMa6alSgxtcv9Ro1/33mI\nhGxHLZQuRbXPt8bLzCV6NKONuesn+IPe/RzTjyaBjV0/Sc3LUVCGnI1VeSJeZUFrc8MVRK5Nu8yy\nfsiKfkhZ7fJo6hYOgny2rB8KjbOXZ0br8WRikx/PXOasUaPrJ7nYW6CkDFjVjvhc9yF+9+hRHAQX\ndt3Y52VzlS8MT7OqH5BXhjw/WqfmTnEhscM7/TkWtBY/nNqMoNlik/6TwblJ7PQjsV02nFmeTNzk\ng5mbDII4X66tUVZ7LOY7Iu5c7dDwsnhpn2+Yq8xpHb4v8w5ltcuGPUc/iJGWx7wyWuWcUePp3BVe\nHh8XKbuGaCnkZJMFrcXDsZ3oQj6FHyUR382U+njmEl/fWWXN2GfTnRblf2yPFa3Jun7A2NdZ1g9F\nTLgmiF8jVxdYTS/DDyR32fGm+Exvjl/M1nndWuSRWE0AxGWXUWAI/kGk433P1/9/Uv3rLTtUkaWA\nPxyKPlXNnaKkCk+9GypU7TIDP05R7XN/8jZXx/PsOznuid8RrMyIDVpU+hSVMVUvQ8PPUFIGlFUB\ngq67Asx81Z7j8fiOOPFFp5ANZ4YHjRqvWdkJlzOISD8gPtBzao+8MmTHneaKPU9R7VNSBMTkxeFp\nlg3xwbVClYaXnTTwi8qIupflc70HeCbzNiVlIJCAXprHEltsRgL3LWeGjDxm2y7xVOoaMxE+7a6I\nf0U/IKuYPJbY5E1riaaf5g3zOKdjdV4cL0/g2JvODDnFFMM62eZeo0ZPNsRJyzfQfZ83rSVyyki0\nTWSHhpfl+5PXiaVccvKYK3aF88YdXrWOUVT61L0pun6SmOyQVcYs601uWLNsuQW6fpKHYrfZdEpc\ns5XIouhwT7xKUnL4YPoGG/ac6N36CT6WfQc/lNEkDz+UGQRxzhtVPrf/AMXpAYdyGivUeSJ1nU2n\nTNdLiLws2aEgCwjzNatCUxXYuQ8at9iIfue79tCGl8UJVcpKj5ovNJhn9EO2XJmN8Sx5dcjAj5NW\nxvihxMNT4uTdkLM8mrpF10+g409YqCeMBktam42IcftIfIuaN8U1q8J9uSo77jQ7LhHrVVQtjyY3\nGQQx0rLFS+MllrQjrthz3LBm+VT2IouZDjvuNI1BmoaXJVBkkfQ7VHgodps3rWMi0ULrUFCHKFKA\nG6jcE7/Dm9Yx9t0cn8q8xSV7noaUZVFts+NOk1bGVANdEM6MPhW1jxkapCNC1avjFU7PNKi6Bdb1\nBlecWbp+gqLa56qTY+TpXLMrpBR7okMFWIk22BfGc6zpB6xqLX53sMhPp1s8OypNWgYVtUPNmyIm\nOwhK3nu7wr8FJ9X3xaaqSx5J2easfkTVS/B4fIcdL8uiLmx3q0ZjclvdneLR5C36QYwVrUk3cq50\n/QQZ2WLHzdENEpw36jT9uJB3aEcU1UGEkTug6mU4HRM2PR+J80adTXeKNb1BUe0zCOIkZYddT2zW\nyUgxcFeGI5B8PpfsHAVlxBOp6wyCOMtqjw3ZYU1vYEg+VS9L00+SlsfclxDkrYafnSDo2n6Cs8Y+\nTT/Oitak5mc5EWvQCkRyZ04ZseVOcT7qOeYUkw1nlpjkckztcKinKatdzkeoOIWAOU1Avc/H9th0\nZjiISkvL1Sbl9km9IdB+BARILEXR32+Yy6zHa6zp+2w4M3wyWeOaKxCIMcnnkj1HRh7zznie0/Ga\nGIjINhtOmYdjVTQJDnydqpdnEMTJKSY37FmeSG4wCGIsqH2eH65TjPqmgyDOg7E615wClUQPK1Qp\nqMPI+STy5BOyQ1oZT+Jxml6G9ViNJXXIc6OTNH3huU9INiVlSCtIcEZvcMWei4Yo4jS75U6hSz5r\n8X2ejNdp+AppyWPby/JSf5Wfn/42ecXiG+aqgJvoooJJSx4X7Tm23WlWtCYAV+x5corJB5I3+NcH\nT/Hj2TeQCal6Wc4Ydbbc4sQJCPBIfJcdN8ec1hHBhG4eVQ6436ihqz4ntUPSssuOlyXUQna8AmeM\nGjVP0LOqXlaoNRQRKX02JkAlW26BVb2BTsCOV2Dd2McKFRQlpOknkQnZjF5HEUXj8nj8Fn8wfoCC\nMuTAF8nAC1oLhYCPJfbYTU/zRHyb23aR+40aG+40shRy6KfJKSOW1R7VyMzxwfguz45KfCJp8pI1\nnMQHFZQh+Uj18N6uv7nT57tZ74vyP0AiLVtcskv4yFQ90bx/MnFL9I/cHK9aFTbsORa0NjnZ5I+a\nF9CkQPRMrTzXrQqjUGcQxLlsLk54jnec/IR3eXm8gBw5k5aiXtfdOOIbtohk+VzrIa6NKzwWq7Np\nl0nL40lC5928eYWQS3YOMxQxzn4k02kGBjV7ClkKeXZwr5imR86SF/rr9IMYCcnGDAxeHQq96CDQ\nop6Yy7Y9Q92d4oY1N2FfmqExyaA/a+wjE7CiH2KGKt8ZiJ9R+65E2a6fiIDaMV4fHhdT/0D/f9s7\n1xi5zrOO/55zmTk7u7M7e/Ve7Hi9thuaJrFp1dKmpaJFQFokaKUKKgFqUSW+hEtBoKQqgiL1A7cC\nHwoUqiKFi4gCNGpVCqItvUBbp7k0ji9x6thx7Nhe73p3Z2d2rmfOefjwvnOyMbFjN+udafT+pNGc\nOXN75p1znvNenuf/ZGLVNc1xJjZZSpeSIRKE0K76L8VFwOiNHm3s5Gg75EJn1JYv8Tm0sZeSX+dS\ne5i7ovOU0xwLwTpPNWdZTvOc7gwx5rUpeo2sJwxQlJhEPeppwIhf41w8RqwBj9XmWU5yeJgkglqa\nZyFYzeQDH9/YzcnGDpqpmUuMNeCRjT324pTn2+vm9z9a28Ow16Rm528TxNiUDFPXgEdre6x8oBBK\nwqHmJEdaczzXGWZXUOFSY5jZoEGsHuP+BhNBheVkmEgSLiUDFL2G1b018o61NE/Ra5i54Za5AD7c\nnCcnCSfbO5gL1ij5NR6r76GaRqwmEZEXc7ixm3PxuEm1LlymrR4rK0NM+GZREEB94zR2+A2WbWjX\noNeijY8nKXPhGrN+i0OVvQCM257k0cZOBm0Fhm46c6UT8d36biKJMxGcL1bvxBPlRGuGShrxfHuM\n0+0pjrV2cqETMBZssJrmWG4XM9nIQFJ8MWF4R9qmxHmsAY80Z1lNhvhmM+WtkcegtG158QKryU1U\n/ner/9dH9wQEMid4Mh7PBKNzkjDotailORaTEUq5BkXPhOoMBS3yNo/bl5TIiyl4HRJMDGuXgnVO\nOUmyOdTuyVL0G+bAC01kwMmOCYBvWk3QrpJ8gkfZrmy21Rzo3QWcHCm35FcISe0KfWDnbb1MeKKu\nVpA4v5oJN4fSoZzmKPoNpoN1RmyJ3+7z5TSXXWxSPMppgVBSJnNVBqVNqp4tNCjZYlNTQ0bDuhU0\n9rNY32m/gkdKJIk9aRLKaYRPSt4zeptNDSj6TRsT2qGa5miqz2hQx8N87zPxMKl6XEgKWRvHatI9\ny8kg1eQFsei6jemsaUg9zRNJh5JXZyRo0NSAGJ+LjWFb6iaiZsslDwdNRoJGJuINMB7WrIP12V9Y\noq0+I4FZgByU2Apx+0yFL/zOscDMcyY2HMpsmzpX9TRgtrDOql2MqaU56mkej5SqPSabai8OXpuq\nXZCppgPU0hw7IlM/qnustNWnluappXlGg5rVNzVi4Av5S2wkkV0oy9NSnzDqsJoGVnUrwGuZ/3zV\n/udtzOJi9zxYSYY43RliMlclxTNtpSEFr52JSoeSEEqHuXyZEb+R6RFUUuPc836H4qbzYjKoZP+P\nEboe4HWD56mnAU0N6ahHM81lYXeAmTqzlTgSvCyOtTtquBkhVQpoktzQrRe8rFMVkUhEviMih0Xk\nmIj8gd0/JiJfEpGT9n5003s+IiLPiMjTIvJTL/cdiXoM25CmOX+DotdgV1DmVHuKO6JzWUmUuwbO\nMew3WekMsW9giTOdIcb8Dfbkl1nILXFruMJssMZroousJhEHcg0ORmdZCFepJgPsy1+inBaYDy9z\npj3JLYFRm0/wskWFmVyZcbv4sje3RNFrsiswix2D0mZQ2qwkQ5T8OlN+ldvCminpYcVcAFbTiOnQ\nVCKYD5eZ9msU/SZ35CpmlRa1hQjNHKFZcS+ZxabOMHtzS5S8Oj8UXmbaNws4+4ON7HsX4xGTtOC3\nqGku66FP+jVCElvpoMOdA2eZ9KuM+Rtm8c4q6/uiLCaD+Jj7rpzc3miJYa9JOS3YUijCHTnTo6+m\nEQcKz3GhM8pMaP6b7sr+rfmL5oKjHpXUaAREXsx8aKp8nu+UWLSLZAVbewngRwqnsmqwOwaqzAZ2\ncTE2NZ/eUTzOaFCz4ibrLISrHCiYqZCmhowE5j+4I3qe2aDKUjJkZOmSIkWvwXy4SlN9bo/OWYHx\nSqYyNRVUzfxrMsxsvsz5ZISVZJBBr03kxdwSmFC2BGHc32Dc32AlHWA1KbArt5JVbt0zcDnLvS96\nDebCNS7Y+fQ7onO8JSrb6gMVYpvVtDd3iQE/ppwOkM/HLHZMr3DSryKJMBussZiYz6yneWaDKvtC\n837AZLz5bWaDNS50RqilefblF+0Iz6eZhswFa1lJm1A63BY2ibyYiaBCPuiwP7fIlF9lR7hOSMIb\no7OZHqqPcr41mkW9ADbxoZ6FTU0G5qLVLbNzvlPi3+sRP10w5/HyTalRZcup3MitB1xPT7UFvFNV\nDwAHgbtF5M3AfcBXVHU/8BX7GFuN8P3A64C7gb+yVQuvSiQdnmztYtyrcSKeoJwWON6aoZ6aoWpT\nQ8a9Gmc6QzzdnOFse5yFvHFyJ9smwL+pIU/H41zojGa93i/Xd1BJjXBINwRn0q9aXUojPnG2Y4LT\nm2nISjLEgnVo3cfHW3N83caufq89zRPNW8yigfqciSc43DbD+0ONBRKEXTYWctKvcKw1y/nOKEda\nM9waXeSR1jhHmruYDarszy1ytjNmSlWrl60S77LlN87Ek3yzMc/Zzhglr87j7QliDTjdmrKhYQEH\norMkahY4UvU4HU8wH5Ypbxp6nYknONmeNrWG4gna+FnVS09SUvWoJBFnO2PE6rPUKWblaGINONEe\ntWFO5uRa7hS53OnqpZqMo25PLPJis2AUj/Ld+m5OtCdZ7Qxm6mE5SThU3QcYp9gtIX6iNcuF2ggn\n21OZ7ufhxi2cj0d588ApjjfnONGe4bQNFwMoek325pY4FU/RVp9vWXWxkmdGCFNBlXMdI5qd4rGU\nFDnR3kGKZA5w0EYhXI6HKHlGmOWx2jyPb+zmrK1KW00HeLi+j2OtnUz7NUpeg8W4ZIRlvAaL9v+f\n89ezC03Ra/JobYFYAx5umlpVj7dmWQgv46GUk0G+sWiG7zPFKuP+C6XZERP8X/SaLOSWAPhWYw+P\nt6aYDdZ5tmWiW24feJ6T7WlKXp294VpWNr3ktdgfrrGSDGWlUS7Eo/xPc4LljkkNfn59hHJS4FQ8\nBZCVQDGFDgeNkpqV71vslKi28pxsT3OkNWcWa+2Fb8qvmrAwNWO+kCSLY530bsacKmiqN3TrBS+7\nUGWrCHb78qG9KabK4I/Z/fcDXwPutfsfUNUW8KyIPIOpWvjta33PfLhs0+gqrCTmRKzYxQ6P1A6P\nzHB+xBbRuyu6RE1XuBCPZmLRJkXRpI228e2BVGE6NL23osTEXosdwTpTto7PoHUMJa9OOS2Q0yTL\nSy96DWJMDvx0UM6K5nmSMhusmTLYYWCGxTZGdj5cZzEpMJ+7nKVpribmxI1zl0lUTGHDYJ2S16Sc\nRtzirVJOc0z6FRI8hj0zFWDiYY3aUy3NMxGY53cFdU6080wHVRNrG1SY9teJ1ctSFxN5Ybvk17Mk\niZyfMBs0qKY+RS/JhvmX4hIl36hq+Yky6VcJJSUnCWN+k0oacWv+Ik+3Zmx9+Y5Ry0+GsuFeijAb\nrhF5sSmA6DeJJGE+vEyOlN3RCpFNja2mA8wFa0Rem52DZeaCNcppgXF/gyQ0Tng5KTIZmNpjkcSs\npIMMe03GbVtP20J3kcQsBBu0FEJJslpS036LZhxSCFr4pNRyOUqemeoxK+I+ndSn6LXxaTGXX2O9\nY4oe5iVh2q9QyUUmTlmUxA6tp4MNEvXoqM9CWKGamjpU3Z76ztwqkcQUvFZWv2vaT9gRrpu0zoE6\nY36d9VZkpnI8kw2W5tTO3Zvjoug1M+m+ehoyGtQoeU0WOyOUfBOXDVgVshpFzwx5x/0NRqzC2my4\nxrStxVZJIwo5czyYaTBzAR6WFsN+i5n8enaxmfQbNLXM2ECdeZulVZSY1KsR+0Y6MfLa2aLUDn+D\n5bTAU+06r83dhJ4q9Kz3eSNcl0qV7Wk+BuwD/lJV7xWRsqqW7PMCrKlqSUQ+CRxS1X+0z30G+A9V\n/dcrPvNXMHW0AW4FVoCbFNy2ZUzgbHyl9Lt94GzcKm5V1eJWfZiI/Cfmd98Il1X17q2y4Xq4rpAq\nVU2AgyJSAh4SkduveF5F5Ib62raqYVbZUEQe3W6JrhvF2fjK6Xf7wNm4VVxRGvoVs93O8fvlhlb/\nVbUMfBUzV3pJRGYA7P2SfdkNVx90OByOVwvXs/o/aXuoiMgA8BPACUyVwQ/Yl30A+Jzd/jzwfhHJ\n2yqF+4HvbLXhDofD0Y9cz/B/Brjfzqt6wIOq+gUR+TbwoIh8CHgO+DkAW43wQeA40AHusdMHL8ff\nvvxLeo6z8ZXT7/aBs3Gr+EGwccvpi3IqDofD8WqhbzKqHA6H49WAc6oOh8OxhfTcqYrI3Tad9RkR\nua/X9nQRkTMickREnuiGhlwrNXebbPo7EVkSkaOb9m1ZuvBNtPFjInLetuUTIvLuHtu4S0S+KiLH\nber1b9j9fdGW17Cvb9pxO9LXf2BR1Z7dAB84BSwAOeAwcFsvbdpk2xlg4op9fwzcZ7fvA/5om216\nO/B64OjL2QTcZtszD+yx7ez3yMaPAb/9Eq/tlY0zwOvtdhH4nrWlL9ryGvb1TTsCAgzZ7RB4GHhz\nv7RhL2+97qm+CXhGVU+raht4AJPm2q/8LCYlF3v/nu38clX9BrB6nTZl6cKq+izQTRfuhY1Xo1c2\nXlTVx+12FXgKmKNP2vIa9l2NbW9HNVwtfb3nbdhLeu1U54Bzmx4/z7UPnu1EgS+LyGM2pRZgh6pe\ntNuLwI7emPYirmZTv7Xtr4nIk3Z6oDsk7LmNIjIP/DCmp9V3bXmFfdBH7Sgivog8gUn8+ZKq9mUb\nbje9dqr9zNtU9SDwLuAeEXn75ifVjGn6Kh6tH22y/DVmiucgcBH4RG/NMYjIEPBvwIdVtbL5uX5o\ny5ewr6/aUVUTe47sBN70Uunr9OfxeFPptVPt25RWVT1v75eAhzBDlaul5vaSvk8XVtVL9gRMgU/z\nwrCvZzaKSIhxWP+kqp+1u/umLV/Kvn5sR2uXS1/fRK+d6iPAfhHZIyI5jA7r53tsEyIyKCLF7jbw\nk8BRrp6a20v6Pl24e5JZ3otpS+iRjSIiwGeAp1T1zzY91RdteTX7+qkdxaWvX51er5QB78asbp4C\nPtpre6xNC5iVysPAsa5dwDhGkPsk8GVgbJvt+mfMsC/GzEl96Fo2AR+17fo08K4e2vgPwBHgSczJ\nNdNjG9+GGZY+CTxhb+/ul7a8hn19047AncB3rS1Hgd+z+/uiDXt5c2mqDofDsYX0evjvcDgcryqc\nU3U4HI4txDlVh8Ph2EKcU3U4HI4txDlVh8Ph2EKcU3X8P0RkWkQeEJFTNk33iyLymhv8jPeIyG03\ny0aHo19xTtXxImzg+UPA11R1r6q+AfgIN65z8B6MMtG2Iabkj8PRU5xTdVzJO4BYVT/V3aGqhwFf\nRL7Q3ScinxSRD9rtP7Tan0+KyJ+KyF3AzwB/YnU/94rIQRE5ZF/zUFcMRES+JiJ/LiKPishTIvJG\nEfms1eP8+Kbv+0Wr3/mEiPxN14GKyIaIfEJEDgNv2Y4GcjiuhXOqjiu5HXjsel8sIuOYlMnXqeqd\nwMdV9VuYjJ/fUdWDqnoK+HvgXvuaI8Dvb/qYtpoa9p/CpDXeY+34oIiMi8hrgZ8H3qpGwCMBfsG+\ndxB4WFUPqOr/fv8/2+HYGq6nmqrDcS3WgSbwGduT/cKVLxCREaCkql+3u1jdLwkAAAFHSURBVO4H\n/mXTS7p6D0eAY2ql40TkNEaE423AG4BHzOwEA7wg1JFghEccjr7AOVXHlRwD3vcS+zu8eGQTAahq\nR0TeBPy4fd+vAu+8we9s2ft003b3cYBRmb9fVT/yEu9t6vWVQHc4tgU3/HdcyX8D+U3C3IjInRjH\ndptVGSphnGhX83NEVb8I/CZwwL6tiikFgqquA2si8qP2uV8Cur3W6+ErwPtEZMp+55iI7P5+f6DD\ncTNxPVXHi1BVFZH3An8hIvdihvZngA8DD2IUiZ7FKBSBcZyfE5EI43h/y+5/APi0iPw6pgf7AeBT\nIlIATgO/fAM2HReR3wX+S0Q8jALWPcBzr+S3Ohw3A6dS5XA4HFuIG/47HA7HFuKcqsPhcGwhzqk6\nHA7HFuKcqsPhcGwhzqk6HA7HFuKcqsPhcGwhzqk6HA7HFvJ/NWn38bA5GU4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf6b2cb400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(pv_corr)\n",
    "plt.colorbar()\n",
    "plt.tight_layout()\n",
    "plt.xlabel('Customer')\n",
    "plt.title('Correlation between PV productions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Effect of the distance on the correlation\n",
    "\n",
    "distance estimated with geocoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import combinations # Combinatoric generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>dlat_km</th>\n",
       "      <th>dlng_km</th>\n",
       "      <th>area_km2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>-33.887887</td>\n",
       "      <td>151.196402</td>\n",
       "      <td>1.178955</td>\n",
       "      <td>1.766613</td>\n",
       "      <td>2.082757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>-33.882559</td>\n",
       "      <td>151.213158</td>\n",
       "      <td>2.039637</td>\n",
       "      <td>2.559093</td>\n",
       "      <td>5.219621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-33.921990</td>\n",
       "      <td>151.207330</td>\n",
       "      <td>3.230835</td>\n",
       "      <td>2.408996</td>\n",
       "      <td>7.783069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>-33.892120</td>\n",
       "      <td>151.230645</td>\n",
       "      <td>3.256143</td>\n",
       "      <td>2.567510</td>\n",
       "      <td>8.360180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>-33.886500</td>\n",
       "      <td>151.243761</td>\n",
       "      <td>1.142317</td>\n",
       "      <td>2.256786</td>\n",
       "      <td>2.577965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>-33.892251</td>\n",
       "      <td>151.271453</td>\n",
       "      <td>2.556738</td>\n",
       "      <td>2.590240</td>\n",
       "      <td>6.622565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>-33.875421</td>\n",
       "      <td>151.265623</td>\n",
       "      <td>2.447767</td>\n",
       "      <td>2.433724</td>\n",
       "      <td>5.957190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>-33.912245</td>\n",
       "      <td>151.239388</td>\n",
       "      <td>3.536777</td>\n",
       "      <td>4.301904</td>\n",
       "      <td>15.214876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>-33.923619</td>\n",
       "      <td>151.253963</td>\n",
       "      <td>3.006322</td>\n",
       "      <td>1.589586</td>\n",
       "      <td>4.778806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2037</th>\n",
       "      <td>-33.880260</td>\n",
       "      <td>151.184019</td>\n",
       "      <td>1.627293</td>\n",
       "      <td>1.831566</td>\n",
       "      <td>2.980495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039</th>\n",
       "      <td>-33.866155</td>\n",
       "      <td>151.173821</td>\n",
       "      <td>1.611081</td>\n",
       "      <td>2.438891</td>\n",
       "      <td>3.929251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>-33.858317</td>\n",
       "      <td>151.178192</td>\n",
       "      <td>2.021969</td>\n",
       "      <td>2.576874</td>\n",
       "      <td>5.210359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2044</th>\n",
       "      <td>-33.921824</td>\n",
       "      <td>151.166538</td>\n",
       "      <td>3.008957</td>\n",
       "      <td>2.849975</td>\n",
       "      <td>8.575452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>-33.856669</td>\n",
       "      <td>151.131581</td>\n",
       "      <td>3.393046</td>\n",
       "      <td>3.526216</td>\n",
       "      <td>11.964616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>-33.852381</td>\n",
       "      <td>151.154885</td>\n",
       "      <td>2.227034</td>\n",
       "      <td>2.105717</td>\n",
       "      <td>4.689504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>-33.892056</td>\n",
       "      <td>151.165081</td>\n",
       "      <td>1.364918</td>\n",
       "      <td>1.390969</td>\n",
       "      <td>1.898558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>-33.812602</td>\n",
       "      <td>151.169451</td>\n",
       "      <td>4.129902</td>\n",
       "      <td>3.717060</td>\n",
       "      <td>15.351093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>-33.686743</td>\n",
       "      <td>151.157798</td>\n",
       "      <td>11.410434</td>\n",
       "      <td>7.300888</td>\n",
       "      <td>83.306303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2076</th>\n",
       "      <td>-33.708586</td>\n",
       "      <td>151.122843</td>\n",
       "      <td>6.834674</td>\n",
       "      <td>5.293553</td>\n",
       "      <td>36.179706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2077</th>\n",
       "      <td>-33.692409</td>\n",
       "      <td>151.099545</td>\n",
       "      <td>10.154743</td>\n",
       "      <td>5.413221</td>\n",
       "      <td>54.969873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2079</th>\n",
       "      <td>-33.670909</td>\n",
       "      <td>151.125756</td>\n",
       "      <td>4.110988</td>\n",
       "      <td>5.738143</td>\n",
       "      <td>23.589434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2081</th>\n",
       "      <td>-33.576913</td>\n",
       "      <td>151.169451</td>\n",
       "      <td>13.864250</td>\n",
       "      <td>6.442107</td>\n",
       "      <td>89.314988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2082</th>\n",
       "      <td>-33.598810</td>\n",
       "      <td>151.134494</td>\n",
       "      <td>13.043921</td>\n",
       "      <td>6.210623</td>\n",
       "      <td>81.010870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>-33.636369</td>\n",
       "      <td>151.221901</td>\n",
       "      <td>15.311430</td>\n",
       "      <td>13.917533</td>\n",
       "      <td>213.097337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2085</th>\n",
       "      <td>-33.729324</td>\n",
       "      <td>151.204416</td>\n",
       "      <td>5.382023</td>\n",
       "      <td>5.333334</td>\n",
       "      <td>28.704129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086</th>\n",
       "      <td>-33.747307</td>\n",
       "      <td>151.224816</td>\n",
       "      <td>3.270810</td>\n",
       "      <td>5.370235</td>\n",
       "      <td>17.565017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2087</th>\n",
       "      <td>-33.773259</td>\n",
       "      <td>151.216073</td>\n",
       "      <td>8.792172</td>\n",
       "      <td>7.007599</td>\n",
       "      <td>61.612010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2088</th>\n",
       "      <td>-33.830378</td>\n",
       "      <td>151.239388</td>\n",
       "      <td>5.558167</td>\n",
       "      <td>3.740941</td>\n",
       "      <td>20.792774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2092</th>\n",
       "      <td>-33.792718</td>\n",
       "      <td>151.242303</td>\n",
       "      <td>2.939093</td>\n",
       "      <td>1.988256</td>\n",
       "      <td>5.843670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2093</th>\n",
       "      <td>-33.805574</td>\n",
       "      <td>151.262708</td>\n",
       "      <td>5.539598</td>\n",
       "      <td>4.581344</td>\n",
       "      <td>25.378803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2262</th>\n",
       "      <td>-33.207558</td>\n",
       "      <td>151.542803</td>\n",
       "      <td>10.025702</td>\n",
       "      <td>10.597135</td>\n",
       "      <td>106.243711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2263</th>\n",
       "      <td>-33.250435</td>\n",
       "      <td>151.507765</td>\n",
       "      <td>10.055257</td>\n",
       "      <td>9.859707</td>\n",
       "      <td>99.141892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>-33.118685</td>\n",
       "      <td>151.455221</td>\n",
       "      <td>13.966461</td>\n",
       "      <td>17.664017</td>\n",
       "      <td>246.703802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2265</th>\n",
       "      <td>-33.064147</td>\n",
       "      <td>151.443547</td>\n",
       "      <td>12.853377</td>\n",
       "      <td>17.758614</td>\n",
       "      <td>228.258167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2278</th>\n",
       "      <td>-32.935137</td>\n",
       "      <td>151.548643</td>\n",
       "      <td>10.730533</td>\n",
       "      <td>7.912314</td>\n",
       "      <td>84.903348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2280</th>\n",
       "      <td>-33.022331</td>\n",
       "      <td>151.671339</td>\n",
       "      <td>7.986453</td>\n",
       "      <td>8.285135</td>\n",
       "      <td>66.168844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2281</th>\n",
       "      <td>-33.117098</td>\n",
       "      <td>151.636274</td>\n",
       "      <td>11.868991</td>\n",
       "      <td>6.381345</td>\n",
       "      <td>75.740126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2282</th>\n",
       "      <td>-32.964824</td>\n",
       "      <td>151.647961</td>\n",
       "      <td>5.322890</td>\n",
       "      <td>3.542357</td>\n",
       "      <td>18.855575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>-33.050132</td>\n",
       "      <td>151.595374</td>\n",
       "      <td>23.566386</td>\n",
       "      <td>13.848476</td>\n",
       "      <td>326.358541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>-32.952447</td>\n",
       "      <td>151.618744</td>\n",
       "      <td>7.014187</td>\n",
       "      <td>6.311125</td>\n",
       "      <td>44.267411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>-32.911035</td>\n",
       "      <td>151.618744</td>\n",
       "      <td>9.180464</td>\n",
       "      <td>9.383040</td>\n",
       "      <td>86.140665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>-32.899507</td>\n",
       "      <td>151.572007</td>\n",
       "      <td>6.054063</td>\n",
       "      <td>6.694867</td>\n",
       "      <td>40.531148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>-32.948140</td>\n",
       "      <td>151.706411</td>\n",
       "      <td>3.544538</td>\n",
       "      <td>6.327006</td>\n",
       "      <td>22.426316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>-32.975467</td>\n",
       "      <td>151.712257</td>\n",
       "      <td>7.704252</td>\n",
       "      <td>7.667515</td>\n",
       "      <td>59.072465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>-32.949701</td>\n",
       "      <td>151.744414</td>\n",
       "      <td>3.377046</td>\n",
       "      <td>4.135951</td>\n",
       "      <td>13.967296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>-32.922585</td>\n",
       "      <td>151.734182</td>\n",
       "      <td>2.823917</td>\n",
       "      <td>2.368471</td>\n",
       "      <td>6.688367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>-32.915366</td>\n",
       "      <td>151.757571</td>\n",
       "      <td>1.915366</td>\n",
       "      <td>1.230935</td>\n",
       "      <td>2.357690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2294</th>\n",
       "      <td>-32.912333</td>\n",
       "      <td>151.766343</td>\n",
       "      <td>2.336039</td>\n",
       "      <td>1.667176</td>\n",
       "      <td>3.894587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>-32.867427</td>\n",
       "      <td>151.799972</td>\n",
       "      <td>7.795498</td>\n",
       "      <td>6.339407</td>\n",
       "      <td>49.418839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2296</th>\n",
       "      <td>-32.912500</td>\n",
       "      <td>151.745876</td>\n",
       "      <td>1.416879</td>\n",
       "      <td>1.543001</td>\n",
       "      <td>2.186245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>-32.927216</td>\n",
       "      <td>151.779501</td>\n",
       "      <td>4.915783</td>\n",
       "      <td>4.057959</td>\n",
       "      <td>19.948049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>-32.925233</td>\n",
       "      <td>151.750261</td>\n",
       "      <td>2.804992</td>\n",
       "      <td>2.538046</td>\n",
       "      <td>7.119198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2305</th>\n",
       "      <td>-32.927424</td>\n",
       "      <td>151.706411</td>\n",
       "      <td>2.894259</td>\n",
       "      <td>5.646591</td>\n",
       "      <td>16.342700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>-32.734411</td>\n",
       "      <td>152.151274</td>\n",
       "      <td>6.333997</td>\n",
       "      <td>9.659977</td>\n",
       "      <td>61.186261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2320</th>\n",
       "      <td>-32.714723</td>\n",
       "      <td>151.536962</td>\n",
       "      <td>30.787729</td>\n",
       "      <td>52.034427</td>\n",
       "      <td>1602.021856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2321</th>\n",
       "      <td>-32.691654</td>\n",
       "      <td>151.443547</td>\n",
       "      <td>40.502852</td>\n",
       "      <td>45.987693</td>\n",
       "      <td>1862.632734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>-32.640042</td>\n",
       "      <td>151.911142</td>\n",
       "      <td>30.439834</td>\n",
       "      <td>59.878643</td>\n",
       "      <td>1822.695927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>-32.938053</td>\n",
       "      <td>151.186932</td>\n",
       "      <td>42.827082</td>\n",
       "      <td>59.754323</td>\n",
       "      <td>2559.103278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>-32.787791</td>\n",
       "      <td>151.455221</td>\n",
       "      <td>13.799735</td>\n",
       "      <td>10.973184</td>\n",
       "      <td>151.427034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>-32.731920</td>\n",
       "      <td>150.860936</td>\n",
       "      <td>113.834561</td>\n",
       "      <td>110.733301</td>\n",
       "      <td>12605.276700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                lat         lng     dlat_km     dlng_km      area_km2\n",
       "postcode                                                             \n",
       "2008     -33.887887  151.196402    1.178955    1.766613      2.082757\n",
       "2010     -33.882559  151.213158    2.039637    2.559093      5.219621\n",
       "2018     -33.921990  151.207330    3.230835    2.408996      7.783069\n",
       "2021     -33.892120  151.230645    3.256143    2.567510      8.360180\n",
       "2025     -33.886500  151.243761    1.142317    2.256786      2.577965\n",
       "2026     -33.892251  151.271453    2.556738    2.590240      6.622565\n",
       "2029     -33.875421  151.265623    2.447767    2.433724      5.957190\n",
       "2031     -33.912245  151.239388    3.536777    4.301904     15.214876\n",
       "2034     -33.923619  151.253963    3.006322    1.589586      4.778806\n",
       "2037     -33.880260  151.184019    1.627293    1.831566      2.980495\n",
       "2039     -33.866155  151.173821    1.611081    2.438891      3.929251\n",
       "2041     -33.858317  151.178192    2.021969    2.576874      5.210359\n",
       "2044     -33.921824  151.166538    3.008957    2.849975      8.575452\n",
       "2046     -33.856669  151.131581    3.393046    3.526216     11.964616\n",
       "2047     -33.852381  151.154885    2.227034    2.105717      4.689504\n",
       "2048     -33.892056  151.165081    1.364918    1.390969      1.898558\n",
       "2066     -33.812602  151.169451    4.129902    3.717060     15.351093\n",
       "2074     -33.686743  151.157798   11.410434    7.300888     83.306303\n",
       "2076     -33.708586  151.122843    6.834674    5.293553     36.179706\n",
       "2077     -33.692409  151.099545   10.154743    5.413221     54.969873\n",
       "2079     -33.670909  151.125756    4.110988    5.738143     23.589434\n",
       "2081     -33.576913  151.169451   13.864250    6.442107     89.314988\n",
       "2082     -33.598810  151.134494   13.043921    6.210623     81.010870\n",
       "2084     -33.636369  151.221901   15.311430   13.917533    213.097337\n",
       "2085     -33.729324  151.204416    5.382023    5.333334     28.704129\n",
       "2086     -33.747307  151.224816    3.270810    5.370235     17.565017\n",
       "2087     -33.773259  151.216073    8.792172    7.007599     61.612010\n",
       "2088     -33.830378  151.239388    5.558167    3.740941     20.792774\n",
       "2092     -33.792718  151.242303    2.939093    1.988256      5.843670\n",
       "2093     -33.805574  151.262708    5.539598    4.581344     25.378803\n",
       "...             ...         ...         ...         ...           ...\n",
       "2262     -33.207558  151.542803   10.025702   10.597135    106.243711\n",
       "2263     -33.250435  151.507765   10.055257    9.859707     99.141892\n",
       "2264     -33.118685  151.455221   13.966461   17.664017    246.703802\n",
       "2265     -33.064147  151.443547   12.853377   17.758614    228.258167\n",
       "2278     -32.935137  151.548643   10.730533    7.912314     84.903348\n",
       "2280     -33.022331  151.671339    7.986453    8.285135     66.168844\n",
       "2281     -33.117098  151.636274   11.868991    6.381345     75.740126\n",
       "2282     -32.964824  151.647961    5.322890    3.542357     18.855575\n",
       "2283     -33.050132  151.595374   23.566386   13.848476    326.358541\n",
       "2284     -32.952447  151.618744    7.014187    6.311125     44.267411\n",
       "2285     -32.911035  151.618744    9.180464    9.383040     86.140665\n",
       "2286     -32.899507  151.572007    6.054063    6.694867     40.531148\n",
       "2289     -32.948140  151.706411    3.544538    6.327006     22.426316\n",
       "2290     -32.975467  151.712257    7.704252    7.667515     59.072465\n",
       "2291     -32.949701  151.744414    3.377046    4.135951     13.967296\n",
       "2292     -32.922585  151.734182    2.823917    2.368471      6.688367\n",
       "2293     -32.915366  151.757571    1.915366    1.230935      2.357690\n",
       "2294     -32.912333  151.766343    2.336039    1.667176      3.894587\n",
       "2295     -32.867427  151.799972    7.795498    6.339407     49.418839\n",
       "2296     -32.912500  151.745876    1.416879    1.543001      2.186245\n",
       "2300     -32.927216  151.779501    4.915783    4.057959     19.948049\n",
       "2303     -32.925233  151.750261    2.804992    2.538046      7.119198\n",
       "2305     -32.927424  151.706411    2.894259    5.646591     16.342700\n",
       "2315     -32.734411  152.151274    6.333997    9.659977     61.186261\n",
       "2320     -32.714723  151.536962   30.787729   52.034427   1602.021856\n",
       "2321     -32.691654  151.443547   40.502852   45.987693   1862.632734\n",
       "2324     -32.640042  151.911142   30.439834   59.878643   1822.695927\n",
       "2325     -32.938053  151.186932   42.827082   59.754323   2559.103278\n",
       "2326     -32.787791  151.455221   13.799735   10.973184    151.427034\n",
       "2330     -32.731920  150.860936  113.834561  110.733301  12605.276700\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pcodes = pd.read_csv('postcodes/postcodes.csv', comment='#', index_col='postcode')\n",
    "df_pcodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Postcode of customers:\n",
    "\n",
    "* some post codes have many customers (2259, 2261: Central Coast area, 2290: New Castle)\n",
    "* many only one or two customer per postcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2259    28\n",
       "2261    21\n",
       "2290    13\n",
       "2093     9\n",
       "2262     8\n",
       "2204     7\n",
       "2126     6\n",
       "2100     6\n",
       "2263     6\n",
       "2291     6\n",
       "2076     6\n",
       "2289     6\n",
       "2086     5\n",
       "2280     5\n",
       "2077     5\n",
       "2074     5\n",
       "2285     5\n",
       "2137     5\n",
       "2305     5\n",
       "2190     4\n",
       "2282     4\n",
       "2087     4\n",
       "2251     4\n",
       "2066     4\n",
       "2284     4\n",
       "2320     3\n",
       "2228     3\n",
       "2325     3\n",
       "2265     3\n",
       "2230     3\n",
       "        ..\n",
       "2084     1\n",
       "2029     1\n",
       "2081     1\n",
       "2031     1\n",
       "2034     1\n",
       "2039     1\n",
       "2324     1\n",
       "2231     1\n",
       "2315     1\n",
       "2135     1\n",
       "2264     1\n",
       "2278     1\n",
       "2224     1\n",
       "2281     1\n",
       "2286     1\n",
       "2216     1\n",
       "2211     1\n",
       "2209     1\n",
       "2292     1\n",
       "2207     1\n",
       "2294     1\n",
       "2295     1\n",
       "2200     1\n",
       "2198     1\n",
       "2196     1\n",
       "2296     1\n",
       "2162     1\n",
       "2154     1\n",
       "2046     1\n",
       "2047     1\n",
       "Name: Postcode, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_code = d_raw.groupby('Customer')['Postcode'].min()\n",
    "cust_code.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf70360828>"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ppvGUBQAY1CiBvkPSv69b/lG1DgAwBY6I4Z5ov1/SDRHxB9Xy7ZJ+KyI+es64OUlz1eJb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\nAEn8L4jn5ee9bsuiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfad19da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_code.value_counts().hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helper: compute distance between two places given as (lat, long)\n",
    "\n",
    "use formula \"haversine formula\" from [Great-circle_distance](https://en.wikipedia.org/wiki/Great-circle_distance#Computational_formulas)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114.24202913650407, 36.929388045354493)"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def great_circle(latlong1, latlong2):\n",
    "    '''great circle distance between two points on a spherical earth\n",
    "    \n",
    "    longitudes and latitudes in degrees. Distance in km.\n",
    "    '''\n",
    "    R = 6371 # km\n",
    "    d2r = np.deg2rad\n",
    "    from numpy import cos, sin, arcsin, sqrt\n",
    "    \n",
    "    lat1, lng1 = latlong1\n",
    "    lat2, lng2 = latlong2\n",
    "    dlat = d2r(lat2 - lat1)\n",
    "    dlng = d2r(lng2 - lng1)\n",
    "    \n",
    "    cos12 = cos(d2r(lat1))*cos(d2r(lat2))\n",
    "                       \n",
    "    a = sin(dlat/2)**2 + cos12*np.sin(dlng/2)**2\n",
    "    ang = 2*arcsin(sqrt(a))\n",
    "    return R*ang\n",
    "\n",
    "cities = {\n",
    "    'Newcastle': (-32.933, 151.733),\n",
    "    'Cessnock': (-32.834, 151.3555),\n",
    "    'Gosford': (-33.433, 151.33),\n",
    "    'Sydney':  (-33.856, 151.1925),\n",
    "    'Paris': (48.8571, 2.35191),\n",
    "    'Rennes': (48.11449, -1.67902)\n",
    "}\n",
    "#great_circle(cities['Rennes'], cities['Paris']) # check 309 km\n",
    "great_circle(cities['Newcastle'], cities['Sydney']), great_circle(cities['Newcastle'], cities['Cessnock'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute distances for each pair of customer (***takes ~ 1min***)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pv_corr_flat = []\n",
    "for (c1, c2) in combinations(customers, 2):\n",
    "    p1, p2 = cust_code[c1], cust_code[c2]\n",
    "    d = great_circle(\n",
    "        (df_pcodes.loc[p1][['lat','lng']]),\n",
    "        (df_pcodes.loc[p2][['lat','lng']]),\n",
    "        )\n",
    "    pv_corr_flat.append([c1, c2, pv_corr.loc[c1,c2], p1, p2, d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>c1_code</th>\n",
       "      <th>c2_code</th>\n",
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       "      <td>2220</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "      <td>2261</td>\n",
       "      <td>48.166077</td>\n",
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       "      <td>2230</td>\n",
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       "      <td>2076</td>\n",
       "      <td>2262</td>\n",
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       "      <td>0.853687</td>\n",
       "      <td>2076</td>\n",
       "      <td>2026</td>\n",
       "      <td>24.609799</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.925588</td>\n",
       "      <td>2076</td>\n",
       "      <td>2218</td>\n",
       "      <td>29.207309</td>\n",
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       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.905207</td>\n",
       "      <td>2076</td>\n",
       "      <td>2261</td>\n",
       "      <td>48.166077</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
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       "      <td>2076</td>\n",
       "      <td>2262</td>\n",
       "      <td>67.982245</td>\n",
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       "      <td>2076</td>\n",
       "      <td>2265</td>\n",
       "      <td>77.598327</td>\n",
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       "      <td>2076</td>\n",
       "      <td>2208</td>\n",
       "      <td>25.593212</td>\n",
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       "      <th>15</th>\n",
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       "      <td>0.848227</td>\n",
       "      <td>2076</td>\n",
       "      <td>2229</td>\n",
       "      <td>37.520871</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.916593</td>\n",
       "      <td>2076</td>\n",
       "      <td>2085</td>\n",
       "      <td>7.889052</td>\n",
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       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
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       "      <td>2076</td>\n",
       "      <td>2259</td>\n",
       "      <td>59.737864</td>\n",
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       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.881282</td>\n",
       "      <td>2076</td>\n",
       "      <td>2259</td>\n",
       "      <td>59.737864</td>\n",
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       "      <td>2076</td>\n",
       "      <td>2250</td>\n",
       "      <td>40.288346</td>\n",
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       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
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       "      <td>2076</td>\n",
       "      <td>2283</td>\n",
       "      <td>85.356468</td>\n",
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       "      <th>21</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.869679</td>\n",
       "      <td>2076</td>\n",
       "      <td>2263</td>\n",
       "      <td>62.207342</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0.873059</td>\n",
       "      <td>2076</td>\n",
       "      <td>2305</td>\n",
       "      <td>102.396381</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.876550</td>\n",
       "      <td>2076</td>\n",
       "      <td>2285</td>\n",
       "      <td>99.941546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0.868196</td>\n",
       "      <td>2076</td>\n",
       "      <td>2300</td>\n",
       "      <td>106.168125</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0.928436</td>\n",
       "      <td>2076</td>\n",
       "      <td>2093</td>\n",
       "      <td>16.837281</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0.869201</td>\n",
       "      <td>2076</td>\n",
       "      <td>2261</td>\n",
       "      <td>48.166077</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.857186</td>\n",
       "      <td>2076</td>\n",
       "      <td>2259</td>\n",
       "      <td>59.737864</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0.875872</td>\n",
       "      <td>2076</td>\n",
       "      <td>2259</td>\n",
       "      <td>59.737864</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0.919485</td>\n",
       "      <td>2076</td>\n",
       "      <td>2025</td>\n",
       "      <td>22.720332</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>44820</th>\n",
       "      <td>292</td>\n",
       "      <td>299</td>\n",
       "      <td>0.849916</td>\n",
       "      <td>2261</td>\n",
       "      <td>2076</td>\n",
       "      <td>48.166077</td>\n",
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       "    <tr>\n",
       "      <th>44821</th>\n",
       "      <td>292</td>\n",
       "      <td>300</td>\n",
       "      <td>0.867561</td>\n",
       "      <td>2261</td>\n",
       "      <td>2086</td>\n",
       "      <td>47.113665</td>\n",
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       "    <tr>\n",
       "      <th>44822</th>\n",
       "      <td>293</td>\n",
       "      <td>294</td>\n",
       "      <td>0.869933</td>\n",
       "      <td>2290</td>\n",
       "      <td>2228</td>\n",
       "      <td>131.003737</td>\n",
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       "    <tr>\n",
       "      <th>44823</th>\n",
       "      <td>293</td>\n",
       "      <td>295</td>\n",
       "      <td>0.838073</td>\n",
       "      <td>2290</td>\n",
       "      <td>2126</td>\n",
       "      <td>104.399972</td>\n",
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       "    <tr>\n",
       "      <th>44824</th>\n",
       "      <td>293</td>\n",
       "      <td>296</td>\n",
       "      <td>0.848763</td>\n",
       "      <td>2290</td>\n",
       "      <td>2087</td>\n",
       "      <td>99.961741</td>\n",
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       "    <tr>\n",
       "      <th>44825</th>\n",
       "      <td>293</td>\n",
       "      <td>297</td>\n",
       "      <td>0.890257</td>\n",
       "      <td>2290</td>\n",
       "      <td>2261</td>\n",
       "      <td>50.124813</td>\n",
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       "    <tr>\n",
       "      <th>44826</th>\n",
       "      <td>293</td>\n",
       "      <td>298</td>\n",
       "      <td>0.857741</td>\n",
       "      <td>2290</td>\n",
       "      <td>2216</td>\n",
       "      <td>120.613338</td>\n",
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       "    <tr>\n",
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       "      <td>0.859926</td>\n",
       "      <td>2290</td>\n",
       "      <td>2076</td>\n",
       "      <td>98.199180</td>\n",
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       "    <tr>\n",
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       "      <td>300</td>\n",
       "      <td>0.875327</td>\n",
       "      <td>2290</td>\n",
       "      <td>2086</td>\n",
       "      <td>97.031741</td>\n",
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       "    <tr>\n",
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       "      <td>294</td>\n",
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       "      <td>0.901524</td>\n",
       "      <td>2228</td>\n",
       "      <td>2126</td>\n",
       "      <td>34.874961</td>\n",
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       "      <td>294</td>\n",
       "      <td>296</td>\n",
       "      <td>0.896734</td>\n",
       "      <td>2228</td>\n",
       "      <td>2087</td>\n",
       "      <td>31.275624</td>\n",
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       "      <td>294</td>\n",
       "      <td>297</td>\n",
       "      <td>0.894250</td>\n",
       "      <td>2228</td>\n",
       "      <td>2261</td>\n",
       "      <td>81.307744</td>\n",
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       "      <td>298</td>\n",
       "      <td>0.948868</td>\n",
       "      <td>2228</td>\n",
       "      <td>2216</td>\n",
       "      <td>10.393338</td>\n",
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       "      <td>299</td>\n",
       "      <td>0.892560</td>\n",
       "      <td>2228</td>\n",
       "      <td>2076</td>\n",
       "      <td>36.705239</td>\n",
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       "    <tr>\n",
       "      <th>44834</th>\n",
       "      <td>294</td>\n",
       "      <td>300</td>\n",
       "      <td>0.919581</td>\n",
       "      <td>2228</td>\n",
       "      <td>2086</td>\n",
       "      <td>34.266019</td>\n",
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       "    <tr>\n",
       "      <th>44835</th>\n",
       "      <td>295</td>\n",
       "      <td>296</td>\n",
       "      <td>0.914020</td>\n",
       "      <td>2126</td>\n",
       "      <td>2087</td>\n",
       "      <td>16.898330</td>\n",
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       "    <tr>\n",
       "      <th>44836</th>\n",
       "      <td>295</td>\n",
       "      <td>297</td>\n",
       "      <td>0.869279</td>\n",
       "      <td>2126</td>\n",
       "      <td>2261</td>\n",
       "      <td>54.665428</td>\n",
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       "    <tr>\n",
       "      <th>44837</th>\n",
       "      <td>295</td>\n",
       "      <td>298</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>2126</td>\n",
       "      <td>2216</td>\n",
       "      <td>26.862476</td>\n",
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       "    <tr>\n",
       "      <th>44838</th>\n",
       "      <td>295</td>\n",
       "      <td>299</td>\n",
       "      <td>0.864725</td>\n",
       "      <td>2126</td>\n",
       "      <td>2076</td>\n",
       "      <td>7.866807</td>\n",
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       "    <tr>\n",
       "      <th>44839</th>\n",
       "      <td>295</td>\n",
       "      <td>300</td>\n",
       "      <td>0.891264</td>\n",
       "      <td>2126</td>\n",
       "      <td>2086</td>\n",
       "      <td>17.093170</td>\n",
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       "    <tr>\n",
       "      <th>44840</th>\n",
       "      <td>296</td>\n",
       "      <td>297</td>\n",
       "      <td>0.875342</td>\n",
       "      <td>2087</td>\n",
       "      <td>2261</td>\n",
       "      <td>50.079854</td>\n",
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       "    <tr>\n",
       "      <th>44841</th>\n",
       "      <td>296</td>\n",
       "      <td>298</td>\n",
       "      <td>0.908254</td>\n",
       "      <td>2087</td>\n",
       "      <td>2216</td>\n",
       "      <td>20.937021</td>\n",
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       "    <tr>\n",
       "      <th>44842</th>\n",
       "      <td>296</td>\n",
       "      <td>299</td>\n",
       "      <td>0.880829</td>\n",
       "      <td>2087</td>\n",
       "      <td>2076</td>\n",
       "      <td>11.226161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44843</th>\n",
       "      <td>296</td>\n",
       "      <td>300</td>\n",
       "      <td>0.928147</td>\n",
       "      <td>2087</td>\n",
       "      <td>2086</td>\n",
       "      <td>2.996751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44844</th>\n",
       "      <td>297</td>\n",
       "      <td>298</td>\n",
       "      <td>0.888419</td>\n",
       "      <td>2261</td>\n",
       "      <td>2216</td>\n",
       "      <td>70.924219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44845</th>\n",
       "      <td>297</td>\n",
       "      <td>299</td>\n",
       "      <td>0.888383</td>\n",
       "      <td>2261</td>\n",
       "      <td>2076</td>\n",
       "      <td>48.166077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44846</th>\n",
       "      <td>297</td>\n",
       "      <td>300</td>\n",
       "      <td>0.897039</td>\n",
       "      <td>2261</td>\n",
       "      <td>2086</td>\n",
       "      <td>47.113665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44847</th>\n",
       "      <td>298</td>\n",
       "      <td>299</td>\n",
       "      <td>0.877906</td>\n",
       "      <td>2216</td>\n",
       "      <td>2076</td>\n",
       "      <td>27.300705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44848</th>\n",
       "      <td>298</td>\n",
       "      <td>300</td>\n",
       "      <td>0.915851</td>\n",
       "      <td>2216</td>\n",
       "      <td>2086</td>\n",
       "      <td>23.932848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44849</th>\n",
       "      <td>299</td>\n",
       "      <td>300</td>\n",
       "      <td>0.928818</td>\n",
       "      <td>2076</td>\n",
       "      <td>2086</td>\n",
       "      <td>10.366707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>44850 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        c1   c2    corr12  c1_code  c2_code        d_km\n",
       "0        1    2  0.861145     2076     2259   59.737864\n",
       "1        1    3  0.865654     2076     2263   62.207342\n",
       "2        1    4  0.924266     2076     2220   27.857812\n",
       "3        1    5  0.887806     2076     2261   48.166077\n",
       "4        1    6  0.862610     2076     2230   39.377330\n",
       "5        1    7  0.870251     2076     2259   59.737864\n",
       "6        1    8  0.825935     2076     2126    7.866807\n",
       "7        1    9  0.877647     2076     2259   59.737864\n",
       "8        1   10  0.850608     2076     2262   67.982245\n",
       "9        1   11  0.853687     2076     2026   24.609799\n",
       "10       1   12  0.925588     2076     2218   29.207309\n",
       "11       1   13  0.905207     2076     2261   48.166077\n",
       "12       1   14  0.883776     2076     2262   67.982245\n",
       "13       1   15  0.802923     2076     2265   77.598327\n",
       "14       1   16  0.810628     2076     2208   25.593212\n",
       "15       1   17  0.848227     2076     2229   37.520871\n",
       "16       1   18  0.916593     2076     2085    7.889052\n",
       "17       1   19  0.875158     2076     2259   59.737864\n",
       "18       1   20  0.881282     2076     2259   59.737864\n",
       "19       1   21  0.861405     2076     2250   40.288346\n",
       "20       1   22  0.881728     2076     2283   85.356468\n",
       "21       1   23  0.869679     2076     2263   62.207342\n",
       "22       1   24  0.873059     2076     2305  102.396381\n",
       "23       1   25  0.876550     2076     2285   99.941546\n",
       "24       1   26  0.868196     2076     2300  106.168125\n",
       "25       1   27  0.928436     2076     2093   16.837281\n",
       "26       1   28  0.869201     2076     2261   48.166077\n",
       "27       1   29  0.857186     2076     2259   59.737864\n",
       "28       1   30  0.875872     2076     2259   59.737864\n",
       "29       1   31  0.919485     2076     2025   22.720332\n",
       "...    ...  ...       ...      ...      ...         ...\n",
       "44820  292  299  0.849916     2261     2076   48.166077\n",
       "44821  292  300  0.867561     2261     2086   47.113665\n",
       "44822  293  294  0.869933     2290     2228  131.003737\n",
       "44823  293  295  0.838073     2290     2126  104.399972\n",
       "44824  293  296  0.848763     2290     2087   99.961741\n",
       "44825  293  297  0.890257     2290     2261   50.124813\n",
       "44826  293  298  0.857741     2290     2216  120.613338\n",
       "44827  293  299  0.859926     2290     2076   98.199180\n",
       "44828  293  300  0.875327     2290     2086   97.031741\n",
       "44829  294  295  0.901524     2228     2126   34.874961\n",
       "44830  294  296  0.896734     2228     2087   31.275624\n",
       "44831  294  297  0.894250     2228     2261   81.307744\n",
       "44832  294  298  0.948868     2228     2216   10.393338\n",
       "44833  294  299  0.892560     2228     2076   36.705239\n",
       "44834  294  300  0.919581     2228     2086   34.266019\n",
       "44835  295  296  0.914020     2126     2087   16.898330\n",
       "44836  295  297  0.869279     2126     2261   54.665428\n",
       "44837  295  298  0.921391     2126     2216   26.862476\n",
       "44838  295  299  0.864725     2126     2076    7.866807\n",
       "44839  295  300  0.891264     2126     2086   17.093170\n",
       "44840  296  297  0.875342     2087     2261   50.079854\n",
       "44841  296  298  0.908254     2087     2216   20.937021\n",
       "44842  296  299  0.880829     2087     2076   11.226161\n",
       "44843  296  300  0.928147     2087     2086    2.996751\n",
       "44844  297  298  0.888419     2261     2216   70.924219\n",
       "44845  297  299  0.888383     2261     2076   48.166077\n",
       "44846  297  300  0.897039     2261     2086   47.113665\n",
       "44847  298  299  0.877906     2216     2076   27.300705\n",
       "44848  298  300  0.915851     2216     2086   23.932848\n",
       "44849  299  300  0.928818     2076     2086   10.366707\n",
       "\n",
       "[44850 rows x 6 columns]"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pv_corr = pd.DataFrame(pv_corr_flat, columns=['c1', 'c2', 'corr12', 'c1_code', 'c2_code', 'd_km'])\n",
    "df_pv_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VAlDdPh7cEIryRMb9Dis24gpq9llx7lgDzCYmiDhdEwhHcfEkNy91tbrQcaAa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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf67f48400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_pv_corr.d_km, df_pv_corr.corr12, ',', alpha=0.3)\n",
    "plt.xlabel('distance, est. with postcode (km)')\n",
    "plt.ylabel('correlation of PV gen');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+43dmbOP+PN40gHMmVnH1pE/J/UVaDsnHO508aAPqHnoSZj8L0/5F3ZU/558X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\noBzHcZxW4gbKcRzHaSX/D8CfYU4qAekxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf67de7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_pv_corr.d_km, df_pv_corr.corr12, ',', alpha=1, label='data')\n",
    "\n",
    "d = np.linspace(0, 175)\n",
    "plt.plot(d, 0.95-d/2000, lw=3, alpha=0.6, label='0.95-d/2000')\n",
    "\n",
    "plt.legend()\n",
    "plt.ylim(0.7, 1.05)\n",
    "plt.xlabel('distance, est. from postcodes (km)')\n",
    "plt.ylabel('correlation of PV gen');\n",
    "plt.tight_layout()\n",
    "plt.savefig('PV corr distance.png', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Possible refinements:\n",
    "\n",
    "* compare with other \"correlation vs. distance\" reports from the litterature...\n",
    "* filter out the very large postcode areas"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
